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1609.08716 | Juan Francisco Saldarriaga | Juan Francisco Saldarriaga (Columbia University), David A. King
(Arizona State University) | Access to Taxicabs for Unbanked Households: An Exploratory Analysis in
New York City | Presented at the Data For Good Exchange 2016 | null | null | null | cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Taxicabs are a critical aspect of the public transit system in New York City.
The yellow cabs that are ubiquitous in Manhattan are as iconic as the city's
subway system, and in recent years green taxicabs were introduced by the city
to improve taxi service in areas outside of the central business districts and
airports. Approximately 500,000 taxi trips are taken daily, carrying about
800,000 passengers, and not including other livery firms such as Uber, Lyft or
Carmel. Since 2008 yellow taxis have been able to process fare payments with
credit cards, and credits cards are a growing share of total fare payments.
However, the use of credit cards to pay for taxi fares varies widely across
neighborhoods, and there are strong correlations between cash payments for taxi
fares and the presence of unbanked or underbanked populations. These issues are
of concern for policymakers as approximately ten percent of households in the
city are unbanked, and in some neighborhoods the share of unbanked households
is over 50 percent. In this paper we use multiple datasets to explore taxicab
fare payments by neighborhood and examine how access to taxicab services is
associated with use of conventional banking services. There is a clear spatial
dimension to the propensity of riders to pay cash, and we find that both
immigrant status and being 'unbanked' are strong predictors of cash
transactions for taxicabs. These results have implications for local
regulations of the for-hire vehicle industry, particularly in the context of
the rapid growth of services that require credit cards. Without some type of
cash-based payment option taxi services will isolate certain neighborhoods. At
the very least, existing and new providers of transit services must consider
access to mainstream financial products as part of their equity analyses.
| [
{
"version": "v1",
"created": "Wed, 28 Sep 2016 00:34:02 GMT"
}
] | 2016-09-29T00:00:00 | [
[
"Saldarriaga",
"Juan Francisco",
"",
"Columbia University"
],
[
"King",
"David A.",
"",
"Arizona State University"
]
] | TITLE: Access to Taxicabs for Unbanked Households: An Exploratory Analysis in
New York City
ABSTRACT: Taxicabs are a critical aspect of the public transit system in New York City.
The yellow cabs that are ubiquitous in Manhattan are as iconic as the city's
subway system, and in recent years green taxicabs were introduced by the city
to improve taxi service in areas outside of the central business districts and
airports. Approximately 500,000 taxi trips are taken daily, carrying about
800,000 passengers, and not including other livery firms such as Uber, Lyft or
Carmel. Since 2008 yellow taxis have been able to process fare payments with
credit cards, and credits cards are a growing share of total fare payments.
However, the use of credit cards to pay for taxi fares varies widely across
neighborhoods, and there are strong correlations between cash payments for taxi
fares and the presence of unbanked or underbanked populations. These issues are
of concern for policymakers as approximately ten percent of households in the
city are unbanked, and in some neighborhoods the share of unbanked households
is over 50 percent. In this paper we use multiple datasets to explore taxicab
fare payments by neighborhood and examine how access to taxicab services is
associated with use of conventional banking services. There is a clear spatial
dimension to the propensity of riders to pay cash, and we find that both
immigrant status and being 'unbanked' are strong predictors of cash
transactions for taxicabs. These results have implications for local
regulations of the for-hire vehicle industry, particularly in the context of
the rapid growth of services that require credit cards. Without some type of
cash-based payment option taxi services will isolate certain neighborhoods. At
the very least, existing and new providers of transit services must consider
access to mainstream financial products as part of their equity analyses.
| no_new_dataset | 0.906198 |
1609.08740 | Shifeng Zhang | Shifeng Zhang, Jianmin Li, Jinma Guo, and Bo Zhang | Scalable Discrete Supervised Hash Learning with Asymmetric Matrix
Factorization | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hashing method maps similar data to binary hashcodes with smaller hamming
distance, and it has received a broad attention due to its low storage cost and
fast retrieval speed. However, the existing limitations make the present
algorithms difficult to deal with large-scale datasets: (1) discrete
constraints are involved in the learning of the hash function; (2) pairwise or
triplet similarity is adopted to generate efficient hashcodes, resulting both
time and space complexity are greater than O(n^2). To address these issues, we
propose a novel discrete supervised hash learning framework which can be
scalable to large-scale datasets. First, the discrete learning procedure is
decomposed into a binary classifier learning scheme and binary codes learning
scheme, which makes the learning procedure more efficient. Second, we adopt the
Asymmetric Low-rank Matrix Factorization and propose the Fast Clustering-based
Batch Coordinate Descent method, such that the time and space complexity is
reduced to O(n). The proposed framework also provides a flexible paradigm to
incorporate with arbitrary hash function, including deep neural networks and
kernel methods. Experiments on large-scale datasets demonstrate that the
proposed method is superior or comparable with state-of-the-art hashing
algorithms.
| [
{
"version": "v1",
"created": "Wed, 28 Sep 2016 02:37:23 GMT"
}
] | 2016-09-29T00:00:00 | [
[
"Zhang",
"Shifeng",
""
],
[
"Li",
"Jianmin",
""
],
[
"Guo",
"Jinma",
""
],
[
"Zhang",
"Bo",
""
]
] | TITLE: Scalable Discrete Supervised Hash Learning with Asymmetric Matrix
Factorization
ABSTRACT: Hashing method maps similar data to binary hashcodes with smaller hamming
distance, and it has received a broad attention due to its low storage cost and
fast retrieval speed. However, the existing limitations make the present
algorithms difficult to deal with large-scale datasets: (1) discrete
constraints are involved in the learning of the hash function; (2) pairwise or
triplet similarity is adopted to generate efficient hashcodes, resulting both
time and space complexity are greater than O(n^2). To address these issues, we
propose a novel discrete supervised hash learning framework which can be
scalable to large-scale datasets. First, the discrete learning procedure is
decomposed into a binary classifier learning scheme and binary codes learning
scheme, which makes the learning procedure more efficient. Second, we adopt the
Asymmetric Low-rank Matrix Factorization and propose the Fast Clustering-based
Batch Coordinate Descent method, such that the time and space complexity is
reduced to O(n). The proposed framework also provides a flexible paradigm to
incorporate with arbitrary hash function, including deep neural networks and
kernel methods. Experiments on large-scale datasets demonstrate that the
proposed method is superior or comparable with state-of-the-art hashing
algorithms.
| no_new_dataset | 0.947186 |
1609.08758 | Mayu Otani | Mayu Otani, Yuta Nakashima, Esa Rahtu, Janne Heikkil\"a, Naokazu
Yokoya | Video Summarization using Deep Semantic Features | 16 pages, the 13th Asian Conference on Computer Vision (ACCV'16) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a video summarization technique for an Internet video to
provide a quick way to overview its content. This is a challenging problem
because finding important or informative parts of the original video requires
to understand its content. Furthermore the content of Internet videos is very
diverse, ranging from home videos to documentaries, which makes video
summarization much more tough as prior knowledge is almost not available. To
tackle this problem, we propose to use deep video features that can encode
various levels of content semantics, including objects, actions, and scenes,
improving the efficiency of standard video summarization techniques. For this,
we design a deep neural network that maps videos as well as descriptions to a
common semantic space and jointly trained it with associated pairs of videos
and descriptions. To generate a video summary, we extract the deep features
from each segment of the original video and apply a clustering-based
summarization technique to them. We evaluate our video summaries using the
SumMe dataset as well as baseline approaches. The results demonstrated the
advantages of incorporating our deep semantic features in a video summarization
technique.
| [
{
"version": "v1",
"created": "Wed, 28 Sep 2016 03:41:49 GMT"
}
] | 2016-09-29T00:00:00 | [
[
"Otani",
"Mayu",
""
],
[
"Nakashima",
"Yuta",
""
],
[
"Rahtu",
"Esa",
""
],
[
"Heikkilä",
"Janne",
""
],
[
"Yokoya",
"Naokazu",
""
]
] | TITLE: Video Summarization using Deep Semantic Features
ABSTRACT: This paper presents a video summarization technique for an Internet video to
provide a quick way to overview its content. This is a challenging problem
because finding important or informative parts of the original video requires
to understand its content. Furthermore the content of Internet videos is very
diverse, ranging from home videos to documentaries, which makes video
summarization much more tough as prior knowledge is almost not available. To
tackle this problem, we propose to use deep video features that can encode
various levels of content semantics, including objects, actions, and scenes,
improving the efficiency of standard video summarization techniques. For this,
we design a deep neural network that maps videos as well as descriptions to a
common semantic space and jointly trained it with associated pairs of videos
and descriptions. To generate a video summary, we extract the deep features
from each segment of the original video and apply a clustering-based
summarization technique to them. We evaluate our video summaries using the
SumMe dataset as well as baseline approaches. The results demonstrated the
advantages of incorporating our deep semantic features in a video summarization
technique.
| no_new_dataset | 0.945601 |
1609.08824 | Subhro Roy | Subhro Roy, Shyam Upadhyay, Dan Roth | Equation Parsing: Mapping Sentences to Grounded Equations | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Identifying mathematical relations expressed in text is essential to
understanding a broad range of natural language text from election reports, to
financial news, to sport commentaries to mathematical word problems. This paper
focuses on identifying and understanding mathematical relations described
within a single sentence. We introduce the problem of Equation Parsing -- given
a sentence, identify noun phrases which represent variables, and generate the
mathematical equation expressing the relation described in the sentence. We
introduce the notion of projective equation parsing and provide an efficient
algorithm to parse text to projective equations. Our system makes use of a high
precision lexicon of mathematical expressions and a pipeline of structured
predictors, and generates correct equations in $70\%$ of the cases. In $60\%$
of the time, it also identifies the correct noun phrase $\rightarrow$ variables
mapping, significantly outperforming baselines. We also release a new annotated
dataset for task evaluation.
| [
{
"version": "v1",
"created": "Wed, 28 Sep 2016 08:54:05 GMT"
}
] | 2016-09-29T00:00:00 | [
[
"Roy",
"Subhro",
""
],
[
"Upadhyay",
"Shyam",
""
],
[
"Roth",
"Dan",
""
]
] | TITLE: Equation Parsing: Mapping Sentences to Grounded Equations
ABSTRACT: Identifying mathematical relations expressed in text is essential to
understanding a broad range of natural language text from election reports, to
financial news, to sport commentaries to mathematical word problems. This paper
focuses on identifying and understanding mathematical relations described
within a single sentence. We introduce the problem of Equation Parsing -- given
a sentence, identify noun phrases which represent variables, and generate the
mathematical equation expressing the relation described in the sentence. We
introduce the notion of projective equation parsing and provide an efficient
algorithm to parse text to projective equations. Our system makes use of a high
precision lexicon of mathematical expressions and a pipeline of structured
predictors, and generates correct equations in $70\%$ of the cases. In $60\%$
of the time, it also identifies the correct noun phrase $\rightarrow$ variables
mapping, significantly outperforming baselines. We also release a new annotated
dataset for task evaluation.
| new_dataset | 0.956675 |
1609.08938 | Allison Del Giorno | Allison Del Giorno, J. Andrew Bagnell, Martial Hebert | A Discriminative Framework for Anomaly Detection in Large Videos | 14 pages without references, 16 pages with. 7 figures. Accepted to
ECCV 2016 | null | null | null | cs.CV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address an anomaly detection setting in which training sequences are
unavailable and anomalies are scored independently of temporal ordering.
Current algorithms in anomaly detection are based on the classical density
estimation approach of learning high-dimensional models and finding
low-probability events. These algorithms are sensitive to the order in which
anomalies appear and require either training data or early context assumptions
that do not hold for longer, more complex videos. By defining anomalies as
examples that can be distinguished from other examples in the same video, our
definition inspires a shift in approaches from classical density estimation to
simple discriminative learning. Our contributions include a novel framework for
anomaly detection that is (1) independent of temporal ordering of anomalies,
and (2) unsupervised, requiring no separate training sequences. We show that
our algorithm can achieve state-of-the-art results even when we adjust the
setting by removing training sequences from standard datasets.
| [
{
"version": "v1",
"created": "Wed, 28 Sep 2016 14:48:32 GMT"
}
] | 2016-09-29T00:00:00 | [
[
"Del Giorno",
"Allison",
""
],
[
"Bagnell",
"J. Andrew",
""
],
[
"Hebert",
"Martial",
""
]
] | TITLE: A Discriminative Framework for Anomaly Detection in Large Videos
ABSTRACT: We address an anomaly detection setting in which training sequences are
unavailable and anomalies are scored independently of temporal ordering.
Current algorithms in anomaly detection are based on the classical density
estimation approach of learning high-dimensional models and finding
low-probability events. These algorithms are sensitive to the order in which
anomalies appear and require either training data or early context assumptions
that do not hold for longer, more complex videos. By defining anomalies as
examples that can be distinguished from other examples in the same video, our
definition inspires a shift in approaches from classical density estimation to
simple discriminative learning. Our contributions include a novel framework for
anomaly detection that is (1) independent of temporal ordering of anomalies,
and (2) unsupervised, requiring no separate training sequences. We show that
our algorithm can achieve state-of-the-art results even when we adjust the
setting by removing training sequences from standard datasets.
| no_new_dataset | 0.950915 |
1609.09018 | Tobi Baumgartner | Tobi Baumgartner and Jack Culpepper | Deep Architectures for Face Attributes | 11 pages, 2 figures, accepted in "Workshop on Facial Informatics in
conjunction with ACCV '16" | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We train a deep convolutional neural network to perform identity
classification using a new dataset of public figures annotated with age,
gender, ethnicity and emotion labels, and then fine-tune it for attribute
classification. An optimal sharing pattern of computational resources within
this network is determined by experiment, requiring only 1 G flops to produce
all predictions. Rather than fine-tune by relearning weights in one additional
layer after the penultimate layer of the identity network, we try several
different depths for each attribute. We find that prediction of age and emotion
is improved by fine-tuning from earlier layers onward, presumably because
deeper layers are progressively invariant to non-identity related changes in
the input.
| [
{
"version": "v1",
"created": "Wed, 28 Sep 2016 17:57:46 GMT"
}
] | 2016-09-29T00:00:00 | [
[
"Baumgartner",
"Tobi",
""
],
[
"Culpepper",
"Jack",
""
]
] | TITLE: Deep Architectures for Face Attributes
ABSTRACT: We train a deep convolutional neural network to perform identity
classification using a new dataset of public figures annotated with age,
gender, ethnicity and emotion labels, and then fine-tune it for attribute
classification. An optimal sharing pattern of computational resources within
this network is determined by experiment, requiring only 1 G flops to produce
all predictions. Rather than fine-tune by relearning weights in one additional
layer after the penultimate layer of the identity network, we try several
different depths for each attribute. We find that prediction of age and emotion
is improved by fine-tuning from earlier layers onward, presumably because
deeper layers are progressively invariant to non-identity related changes in
the input.
| new_dataset | 0.957238 |
1506.01461 | Matthew Burgess | Matthew Burgess, Eytan Adar, Michael Cafarella | Link-Prediction Enhanced Consensus Clustering for Complex Networks | null | null | 10.1371/journal.pone.0153384 | null | cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many real networks that are inferred or collected from data are incomplete
due to missing edges. Missing edges can be inherent to the dataset (Facebook
friend links will never be complete) or the result of sampling (one may only
have access to a portion of the data). The consequence is that downstream
analyses that consume the network will often yield less accurate results than
if the edges were complete. Community detection algorithms, in particular,
often suffer when critical intra-community edges are missing. We propose a
novel consensus clustering algorithm to enhance community detection on
incomplete networks. Our framework utilizes existing community detection
algorithms that process networks imputed by our link prediction based
algorithm. The framework then merges their multiple outputs into a final
consensus output. On average our method boosts performance of existing
algorithms by 7% on artificial data and 17% on ego networks collected from
Facebook.
| [
{
"version": "v1",
"created": "Thu, 4 Jun 2015 04:18:16 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Burgess",
"Matthew",
""
],
[
"Adar",
"Eytan",
""
],
[
"Cafarella",
"Michael",
""
]
] | TITLE: Link-Prediction Enhanced Consensus Clustering for Complex Networks
ABSTRACT: Many real networks that are inferred or collected from data are incomplete
due to missing edges. Missing edges can be inherent to the dataset (Facebook
friend links will never be complete) or the result of sampling (one may only
have access to a portion of the data). The consequence is that downstream
analyses that consume the network will often yield less accurate results than
if the edges were complete. Community detection algorithms, in particular,
often suffer when critical intra-community edges are missing. We propose a
novel consensus clustering algorithm to enhance community detection on
incomplete networks. Our framework utilizes existing community detection
algorithms that process networks imputed by our link prediction based
algorithm. The framework then merges their multiple outputs into a final
consensus output. On average our method boosts performance of existing
algorithms by 7% on artificial data and 17% on ego networks collected from
Facebook.
| no_new_dataset | 0.951188 |
1511.02647 | Samuel Martin | Corentin Vande Kerckhove, Samuel Martin, Pascal Gend, Peter J.
Rentfrow, Julien M. Hendrickx, and Vincent D. Blondel | Modelling influence and opinion evolution in online collective behaviour | Accepted for publication in PLOS ONE (2016) | null | 10.1371/journal.pone.0157685 | null | cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Opinion evolution and judgment revision are mediated through social
influence. Based on a large crowdsourced in vitro experiment (n=861), it is
shown how a consensus model can be used to predict opinion evolution in online
collective behaviour. It is the first time the predictive power of a
quantitative model of opinion dynamics is tested against a real dataset. Unlike
previous research on the topic, the model was validated on data which did not
serve to calibrate it. This avoids to favor more complex models over more
simple ones and prevents overfitting. The model is parametrized by the
influenceability of each individual, a factor representing to what extent
individuals incorporate external judgments. The prediction accuracy depends on
prior knowledge on the participants' past behaviour. Several situations
reflecting data availability are compared. When the data is scarce, the data
from previous participants is used to predict how a new participant will
behave. Judgment revision includes unpredictable variations which limit the
potential for prediction. A first measure of unpredictability is proposed. The
measure is based on a specific control experiment. More than two thirds of the
prediction errors are found to occur due to unpredictability of the human
judgment revision process rather than to model imperfection.
| [
{
"version": "v1",
"created": "Mon, 9 Nov 2015 11:52:08 GMT"
},
{
"version": "v2",
"created": "Mon, 14 Dec 2015 23:01:57 GMT"
},
{
"version": "v3",
"created": "Fri, 3 Jun 2016 16:33:18 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Kerckhove",
"Corentin Vande",
""
],
[
"Martin",
"Samuel",
""
],
[
"Gend",
"Pascal",
""
],
[
"Rentfrow",
"Peter J.",
""
],
[
"Hendrickx",
"Julien M.",
""
],
[
"Blondel",
"Vincent D.",
""
]
] | TITLE: Modelling influence and opinion evolution in online collective behaviour
ABSTRACT: Opinion evolution and judgment revision are mediated through social
influence. Based on a large crowdsourced in vitro experiment (n=861), it is
shown how a consensus model can be used to predict opinion evolution in online
collective behaviour. It is the first time the predictive power of a
quantitative model of opinion dynamics is tested against a real dataset. Unlike
previous research on the topic, the model was validated on data which did not
serve to calibrate it. This avoids to favor more complex models over more
simple ones and prevents overfitting. The model is parametrized by the
influenceability of each individual, a factor representing to what extent
individuals incorporate external judgments. The prediction accuracy depends on
prior knowledge on the participants' past behaviour. Several situations
reflecting data availability are compared. When the data is scarce, the data
from previous participants is used to predict how a new participant will
behave. Judgment revision includes unpredictable variations which limit the
potential for prediction. A first measure of unpredictability is proposed. The
measure is based on a specific control experiment. More than two thirds of the
prediction errors are found to occur due to unpredictability of the human
judgment revision process rather than to model imperfection.
| no_new_dataset | 0.947914 |
1511.04156 | Josh Merel | Josh Merel, David Carlson, Liam Paninski, John P. Cunningham | Neuroprosthetic decoder training as imitation learning | null | null | 10.1371/journal.pcbi.1004948 | null | stat.ML cs.LG q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neuroprosthetic brain-computer interfaces function via an algorithm which
decodes neural activity of the user into movements of an end effector, such as
a cursor or robotic arm. In practice, the decoder is often learned by updating
its parameters while the user performs a task. When the user's intention is not
directly observable, recent methods have demonstrated value in training the
decoder against a surrogate for the user's intended movement. We describe how
training a decoder in this way is a novel variant of an imitation learning
problem, where an oracle or expert is employed for supervised training in lieu
of direct observations, which are not available. Specifically, we describe how
a generic imitation learning meta-algorithm, dataset aggregation (DAgger, [1]),
can be adapted to train a generic brain-computer interface. By deriving
existing learning algorithms for brain-computer interfaces in this framework,
we provide a novel analysis of regret (an important metric of learning
efficacy) for brain-computer interfaces. This analysis allows us to
characterize the space of algorithmic variants and bounds on their regret
rates. Existing approaches for decoder learning have been performed in the
cursor control setting, but the available design principles for these decoders
are such that it has been impossible to scale them to naturalistic settings.
Leveraging our findings, we then offer an algorithm that combines imitation
learning with optimal control, which should allow for training of arbitrary
effectors for which optimal control can generate goal-oriented control. We
demonstrate this novel and general BCI algorithm with simulated neuroprosthetic
control of a 26 degree-of-freedom model of an arm, a sophisticated and
realistic end effector.
| [
{
"version": "v1",
"created": "Fri, 13 Nov 2015 04:21:33 GMT"
},
{
"version": "v2",
"created": "Mon, 14 Mar 2016 16:39:03 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Merel",
"Josh",
""
],
[
"Carlson",
"David",
""
],
[
"Paninski",
"Liam",
""
],
[
"Cunningham",
"John P.",
""
]
] | TITLE: Neuroprosthetic decoder training as imitation learning
ABSTRACT: Neuroprosthetic brain-computer interfaces function via an algorithm which
decodes neural activity of the user into movements of an end effector, such as
a cursor or robotic arm. In practice, the decoder is often learned by updating
its parameters while the user performs a task. When the user's intention is not
directly observable, recent methods have demonstrated value in training the
decoder against a surrogate for the user's intended movement. We describe how
training a decoder in this way is a novel variant of an imitation learning
problem, where an oracle or expert is employed for supervised training in lieu
of direct observations, which are not available. Specifically, we describe how
a generic imitation learning meta-algorithm, dataset aggregation (DAgger, [1]),
can be adapted to train a generic brain-computer interface. By deriving
existing learning algorithms for brain-computer interfaces in this framework,
we provide a novel analysis of regret (an important metric of learning
efficacy) for brain-computer interfaces. This analysis allows us to
characterize the space of algorithmic variants and bounds on their regret
rates. Existing approaches for decoder learning have been performed in the
cursor control setting, but the available design principles for these decoders
are such that it has been impossible to scale them to naturalistic settings.
Leveraging our findings, we then offer an algorithm that combines imitation
learning with optimal control, which should allow for training of arbitrary
effectors for which optimal control can generate goal-oriented control. We
demonstrate this novel and general BCI algorithm with simulated neuroprosthetic
control of a 26 degree-of-freedom model of an arm, a sophisticated and
realistic end effector.
| no_new_dataset | 0.943138 |
1602.09108 | Philipp H\"ovel | Hartmut H. K. Lentz, Andreas Koher, Philipp H\"ovel, J\"orn Gethmann,
Carola Sauter-Louis, Thomas Selhorst, Franz J. Conraths | Disease spread through animal movements: a static and temporal network
analysis of pig trade in Germany | main text 33 pages, 17 figures, supporting information 7 pages, 7
figures | null | 10.1371/journal.pone.0155196 | null | physics.soc-ph q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Animal trade plays an important role for the spread of infectious
diseases in livestock populations. As a case study, we consider pig trade in
Germany, where trade actors (agricultural premises) form a complex network. The
central question is how infectious diseases can potentially spread within the
system of trade contacts. We address this question by analyzing the underlying
network of animal movements.
Methodology/Findings: The considered pig trade dataset spans several years
and is analyzed with respect to its potential to spread infectious diseases.
Focusing on measurements of network-topological properties, we avoid the usage
of external parameters, since these properties are independent of specific
pathogens. They are on the contrary of great importance for understanding any
general spreading process on this particular network. We analyze the system
using different network models, which include varying amounts of information:
(i) static network, (ii) network as a time series of uncorrelated snapshots,
(iii) temporal network, where causality is explicitly taken into account.
Findings: Our approach provides a general framework for a
topological-temporal characterization of livestock trade networks. We find that
a static network view captures many relevant aspects of the trade system, and
premises can be classified into two clearly defined risk classes. Moreover, our
results allow for an efficient allocation strategy for intervention measures
using centrality measures. Data on trade volume does barely alter the results
and is therefore of secondary importance. Although a static network description
yields useful results, the temporal resolution of data plays an outstanding
role for an in-depth understanding of spreading processes. This applies in
particular for an accurate calculation of the maximum outbreak size.
| [
{
"version": "v1",
"created": "Mon, 29 Feb 2016 19:22:35 GMT"
},
{
"version": "v2",
"created": "Wed, 2 Mar 2016 20:34:20 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Lentz",
"Hartmut H. K.",
""
],
[
"Koher",
"Andreas",
""
],
[
"Hövel",
"Philipp",
""
],
[
"Gethmann",
"Jörn",
""
],
[
"Sauter-Louis",
"Carola",
""
],
[
"Selhorst",
"Thomas",
""
],
[
"Conraths",
"Franz J.",
""
]
] | TITLE: Disease spread through animal movements: a static and temporal network
analysis of pig trade in Germany
ABSTRACT: Background: Animal trade plays an important role for the spread of infectious
diseases in livestock populations. As a case study, we consider pig trade in
Germany, where trade actors (agricultural premises) form a complex network. The
central question is how infectious diseases can potentially spread within the
system of trade contacts. We address this question by analyzing the underlying
network of animal movements.
Methodology/Findings: The considered pig trade dataset spans several years
and is analyzed with respect to its potential to spread infectious diseases.
Focusing on measurements of network-topological properties, we avoid the usage
of external parameters, since these properties are independent of specific
pathogens. They are on the contrary of great importance for understanding any
general spreading process on this particular network. We analyze the system
using different network models, which include varying amounts of information:
(i) static network, (ii) network as a time series of uncorrelated snapshots,
(iii) temporal network, where causality is explicitly taken into account.
Findings: Our approach provides a general framework for a
topological-temporal characterization of livestock trade networks. We find that
a static network view captures many relevant aspects of the trade system, and
premises can be classified into two clearly defined risk classes. Moreover, our
results allow for an efficient allocation strategy for intervention measures
using centrality measures. Data on trade volume does barely alter the results
and is therefore of secondary importance. Although a static network description
yields useful results, the temporal resolution of data plays an outstanding
role for an in-depth understanding of spreading processes. This applies in
particular for an accurate calculation of the maximum outbreak size.
| no_new_dataset | 0.944434 |
1604.00125 | Ziqiang Cao | Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei and Yanran Li | AttSum: Joint Learning of Focusing and Summarization with Neural
Attention | 10 pages, 1 figure | COLING 2016 | null | null | cs.IR cs.CL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Query relevance ranking and sentence saliency ranking are the two main tasks
in extractive query-focused summarization. Previous supervised summarization
systems often perform the two tasks in isolation. However, since reference
summaries are the trade-off between relevance and saliency, using them as
supervision, neither of the two rankers could be trained well. This paper
proposes a novel summarization system called AttSum, which tackles the two
tasks jointly. It automatically learns distributed representations for
sentences as well as the document cluster. Meanwhile, it applies the attention
mechanism to simulate the attentive reading of human behavior when a query is
given. Extensive experiments are conducted on DUC query-focused summarization
benchmark datasets. Without using any hand-crafted features, AttSum achieves
competitive performance. It is also observed that the sentences recognized to
focus on the query indeed meet the query need.
| [
{
"version": "v1",
"created": "Fri, 1 Apr 2016 04:18:39 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Sep 2016 02:22:33 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Cao",
"Ziqiang",
""
],
[
"Li",
"Wenjie",
""
],
[
"Li",
"Sujian",
""
],
[
"Wei",
"Furu",
""
],
[
"Li",
"Yanran",
""
]
] | TITLE: AttSum: Joint Learning of Focusing and Summarization with Neural
Attention
ABSTRACT: Query relevance ranking and sentence saliency ranking are the two main tasks
in extractive query-focused summarization. Previous supervised summarization
systems often perform the two tasks in isolation. However, since reference
summaries are the trade-off between relevance and saliency, using them as
supervision, neither of the two rankers could be trained well. This paper
proposes a novel summarization system called AttSum, which tackles the two
tasks jointly. It automatically learns distributed representations for
sentences as well as the document cluster. Meanwhile, it applies the attention
mechanism to simulate the attentive reading of human behavior when a query is
given. Extensive experiments are conducted on DUC query-focused summarization
benchmark datasets. Without using any hand-crafted features, AttSum achieves
competitive performance. It is also observed that the sentences recognized to
focus on the query indeed meet the query need.
| no_new_dataset | 0.944536 |
1604.02123 | Talayeh Razzaghi | Talayeh Razzaghi, Oleg Roderick, Ilya Safro, Nicholas Marko | Multilevel Weighted Support Vector Machine for Classification on
Healthcare Data with Missing Values | arXiv admin note: substantial text overlap with arXiv:1503.06250 | null | 10.1371/journal.pone.0155119 | null | stat.ML cs.LG stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work is motivated by the needs of predictive analytics on healthcare
data as represented by Electronic Medical Records. Such data is invariably
problematic: noisy, with missing entries, with imbalance in classes of
interests, leading to serious bias in predictive modeling. Since standard data
mining methods often produce poor performance measures, we argue for
development of specialized techniques of data-preprocessing and classification.
In this paper, we propose a new method to simultaneously classify large
datasets and reduce the effects of missing values. It is based on a multilevel
framework of the cost-sensitive SVM and the expected maximization imputation
method for missing values, which relies on iterated regression analyses. We
compare classification results of multilevel SVM-based algorithms on public
benchmark datasets with imbalanced classes and missing values as well as real
data in health applications, and show that our multilevel SVM-based method
produces fast, and more accurate and robust classification results.
| [
{
"version": "v1",
"created": "Thu, 7 Apr 2016 19:19:52 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Razzaghi",
"Talayeh",
""
],
[
"Roderick",
"Oleg",
""
],
[
"Safro",
"Ilya",
""
],
[
"Marko",
"Nicholas",
""
]
] | TITLE: Multilevel Weighted Support Vector Machine for Classification on
Healthcare Data with Missing Values
ABSTRACT: This work is motivated by the needs of predictive analytics on healthcare
data as represented by Electronic Medical Records. Such data is invariably
problematic: noisy, with missing entries, with imbalance in classes of
interests, leading to serious bias in predictive modeling. Since standard data
mining methods often produce poor performance measures, we argue for
development of specialized techniques of data-preprocessing and classification.
In this paper, we propose a new method to simultaneously classify large
datasets and reduce the effects of missing values. It is based on a multilevel
framework of the cost-sensitive SVM and the expected maximization imputation
method for missing values, which relies on iterated regression analyses. We
compare classification results of multilevel SVM-based algorithms on public
benchmark datasets with imbalanced classes and missing values as well as real
data in health applications, and show that our multilevel SVM-based method
produces fast, and more accurate and robust classification results.
| no_new_dataset | 0.949623 |
1605.01584 | Yongchao Liu | Yongchao Liu, Tony Pan, Srinivas Aluru | Parallel Pairwise Correlation Computation On Intel Xeon Phi Clusters | 9 pages, 2 figures, 2 tables, accepted by the SBAC-PAD 2016
conference | null | null | null | cs.DC q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Co-expression network is a critical technique for the identification of
inter-gene interactions, which usually relies on all-pairs correlation (or
similar measure) computation between gene expression profiles across multiple
samples. Pearson's correlation coefficient (PCC) is one widely used technique
for gene co-expression network construction. However, all-pairs PCC computation
is computationally demanding for large numbers of gene expression profiles,
thus motivating our acceleration of its execution using high-performance
computing. In this paper, we present LightPCC, the first parallel and
distributed all-pairs PCC computation on Intel Xeon Phi (Phi) clusters. It
achieves high speed by exploring the SIMD-instruction-level and thread-level
parallelism within Phis as well as accelerator-level parallelism among multiple
Phis. To facilitate balanced workload distribution, we have proposed a general
framework for symmetric all-pairs computation by building bijective functions
between job identifier and coordinate space for the first time. We have
evaluated LightPCC and compared it to two CPU-based counterparts: a sequential
C++ implementation in ALGLIB and an implementation based on a parallel general
matrix-matrix multiplication routine in Intel Math Kernel Library (MKL) (all
use double precision), using a set of gene expression datasets. Performance
evaluation revealed that with one 5110P Phi and 16 Phis, LightPCC runs up to
$20.6\times$ and $218.2\times$ faster than ALGLIB, and up to $6.8\times$ and
$71.4\times$ faster than single-threaded MKL, respectively. In addition,
LightPCC demonstrated good parallel scalability in terms of number of Phis.
Source code of LightPCC is publicly available at
http://lightpcc.sourceforge.net.
| [
{
"version": "v1",
"created": "Thu, 5 May 2016 13:30:28 GMT"
},
{
"version": "v2",
"created": "Fri, 10 Jun 2016 13:35:27 GMT"
},
{
"version": "v3",
"created": "Tue, 27 Sep 2016 00:15:44 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Liu",
"Yongchao",
""
],
[
"Pan",
"Tony",
""
],
[
"Aluru",
"Srinivas",
""
]
] | TITLE: Parallel Pairwise Correlation Computation On Intel Xeon Phi Clusters
ABSTRACT: Co-expression network is a critical technique for the identification of
inter-gene interactions, which usually relies on all-pairs correlation (or
similar measure) computation between gene expression profiles across multiple
samples. Pearson's correlation coefficient (PCC) is one widely used technique
for gene co-expression network construction. However, all-pairs PCC computation
is computationally demanding for large numbers of gene expression profiles,
thus motivating our acceleration of its execution using high-performance
computing. In this paper, we present LightPCC, the first parallel and
distributed all-pairs PCC computation on Intel Xeon Phi (Phi) clusters. It
achieves high speed by exploring the SIMD-instruction-level and thread-level
parallelism within Phis as well as accelerator-level parallelism among multiple
Phis. To facilitate balanced workload distribution, we have proposed a general
framework for symmetric all-pairs computation by building bijective functions
between job identifier and coordinate space for the first time. We have
evaluated LightPCC and compared it to two CPU-based counterparts: a sequential
C++ implementation in ALGLIB and an implementation based on a parallel general
matrix-matrix multiplication routine in Intel Math Kernel Library (MKL) (all
use double precision), using a set of gene expression datasets. Performance
evaluation revealed that with one 5110P Phi and 16 Phis, LightPCC runs up to
$20.6\times$ and $218.2\times$ faster than ALGLIB, and up to $6.8\times$ and
$71.4\times$ faster than single-threaded MKL, respectively. In addition,
LightPCC demonstrated good parallel scalability in terms of number of Phis.
Source code of LightPCC is publicly available at
http://lightpcc.sourceforge.net.
| no_new_dataset | 0.951188 |
1607.03316 | Dirk Weissenborn | Dirk Weissenborn | Separating Answers from Queries for Neural Reading Comprehension | null | null | null | null | cs.CL cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel neural architecture for answering queries, designed to
optimally leverage explicit support in the form of query-answer memories. Our
model is able to refine and update a given query while separately accumulating
evidence for predicting the answer. Its architecture reflects this separation
with dedicated embedding matrices and loosely connected information pathways
(modules) for updating the query and accumulating evidence. This separation of
responsibilities effectively decouples the search for query related support and
the prediction of the answer. On recent benchmark datasets for reading
comprehension, our model achieves state-of-the-art results. A qualitative
analysis reveals that the model effectively accumulates weighted evidence from
the query and over multiple support retrieval cycles which results in a robust
answer prediction.
| [
{
"version": "v1",
"created": "Tue, 12 Jul 2016 11:43:15 GMT"
},
{
"version": "v2",
"created": "Wed, 13 Jul 2016 11:54:46 GMT"
},
{
"version": "v3",
"created": "Tue, 27 Sep 2016 13:37:41 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Weissenborn",
"Dirk",
""
]
] | TITLE: Separating Answers from Queries for Neural Reading Comprehension
ABSTRACT: We present a novel neural architecture for answering queries, designed to
optimally leverage explicit support in the form of query-answer memories. Our
model is able to refine and update a given query while separately accumulating
evidence for predicting the answer. Its architecture reflects this separation
with dedicated embedding matrices and loosely connected information pathways
(modules) for updating the query and accumulating evidence. This separation of
responsibilities effectively decouples the search for query related support and
the prediction of the answer. On recent benchmark datasets for reading
comprehension, our model achieves state-of-the-art results. A qualitative
analysis reveals that the model effectively accumulates weighted evidence from
the query and over multiple support retrieval cycles which results in a robust
answer prediction.
| no_new_dataset | 0.947088 |
1609.08210 | Ferhan Ture | Ferhan Ture and Elizabeth Boschee | Learning to Translate for Multilingual Question Answering | 12 pages. To appear in EMNLP'16 | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In multilingual question answering, either the question needs to be
translated into the document language, or vice versa. In addition to direction,
there are multiple methods to perform the translation, four of which we explore
in this paper: word-based, 10-best, context-based, and grammar-based. We build
a feature for each combination of translation direction and method, and train a
model that learns optimal feature weights. On a large forum dataset consisting
of posts in English, Arabic, and Chinese, our novel learn-to-translate approach
was more effective than a strong baseline (p<0.05): translating all text into
English, then training a classifier based only on English (original or
translated) text.
| [
{
"version": "v1",
"created": "Mon, 26 Sep 2016 22:12:50 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Ture",
"Ferhan",
""
],
[
"Boschee",
"Elizabeth",
""
]
] | TITLE: Learning to Translate for Multilingual Question Answering
ABSTRACT: In multilingual question answering, either the question needs to be
translated into the document language, or vice versa. In addition to direction,
there are multiple methods to perform the translation, four of which we explore
in this paper: word-based, 10-best, context-based, and grammar-based. We build
a feature for each combination of translation direction and method, and train a
model that learns optimal feature weights. On a large forum dataset consisting
of posts in English, Arabic, and Chinese, our novel learn-to-translate approach
was more effective than a strong baseline (p<0.05): translating all text into
English, then training a classifier based only on English (original or
translated) text.
| no_new_dataset | 0.94545 |
1609.08264 | Zhao Kang | Zhao Kang, Chong Peng, Ming Yang, Qiang Cheng | Top-N Recommendation on Graphs | CIKM 2016 | null | 10.1145/2983323.2983649 | null | cs.IR cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recommender systems play an increasingly important role in online
applications to help users find what they need or prefer. Collaborative
filtering algorithms that generate predictions by analyzing the user-item
rating matrix perform poorly when the matrix is sparse. To alleviate this
problem, this paper proposes a simple recommendation algorithm that fully
exploits the similarity information among users and items and intrinsic
structural information of the user-item matrix. The proposed method constructs
a new representation which preserves affinity and structure information in the
user-item rating matrix and then performs recommendation task. To capture
proximity information about users and items, two graphs are constructed.
Manifold learning idea is used to constrain the new representation to be smooth
on these graphs, so as to enforce users and item proximities. Our model is
formulated as a convex optimization problem, for which we need to solve the
well-known Sylvester equation only. We carry out extensive empirical
evaluations on six benchmark datasets to show the effectiveness of this
approach.
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2016 05:45:03 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Kang",
"Zhao",
""
],
[
"Peng",
"Chong",
""
],
[
"Yang",
"Ming",
""
],
[
"Cheng",
"Qiang",
""
]
] | TITLE: Top-N Recommendation on Graphs
ABSTRACT: Recommender systems play an increasingly important role in online
applications to help users find what they need or prefer. Collaborative
filtering algorithms that generate predictions by analyzing the user-item
rating matrix perform poorly when the matrix is sparse. To alleviate this
problem, this paper proposes a simple recommendation algorithm that fully
exploits the similarity information among users and items and intrinsic
structural information of the user-item matrix. The proposed method constructs
a new representation which preserves affinity and structure information in the
user-item rating matrix and then performs recommendation task. To capture
proximity information about users and items, two graphs are constructed.
Manifold learning idea is used to constrain the new representation to be smooth
on these graphs, so as to enforce users and item proximities. Our model is
formulated as a convex optimization problem, for which we need to solve the
well-known Sylvester equation only. We carry out extensive empirical
evaluations on six benchmark datasets to show the effectiveness of this
approach.
| no_new_dataset | 0.945951 |
1609.08286 | Weixiang Shao | Weixiang Shao, Lifang He, Chun-Ta Lu, Xiaokai Wei, Philip S. Yu | Online Unsupervised Multi-view Feature Selection | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the era of big data, it is becoming common to have data with multiple
modalities or coming from multiple sources, known as "multi-view data".
Multi-view data are usually unlabeled and come from high-dimensional spaces
(such as language vocabularies), unsupervised multi-view feature selection is
crucial to many applications. However, it is nontrivial due to the following
challenges. First, there are too many instances or the feature dimensionality
is too large. Thus, the data may not fit in memory. How to select useful
features with limited memory space? Second, how to select features from
streaming data and handles the concept drift? Third, how to leverage the
consistent and complementary information from different views to improve the
feature selection in the situation when the data are too big or come in as
streams? To the best of our knowledge, none of the previous works can solve all
the challenges simultaneously. In this paper, we propose an Online unsupervised
Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming
multi-view data in an online fashion. OMVFS embeds unsupervised feature
selection into a clustering algorithm via NMF with sparse learning. It further
incorporates the graph regularization to preserve the local structure
information and help select discriminative features. Instead of storing all the
historical data, OMVFS processes the multi-view data chunk by chunk and
aggregates all the necessary information into several small matrices. By using
the buffering technique, the proposed OMVFS can reduce the computational and
storage cost while taking advantage of the structure information. Furthermore,
OMVFS can capture the concept drifts in the data streams. Extensive experiments
on four real-world datasets show the effectiveness and efficiency of the
proposed OMVFS method. More importantly, OMVFS is about 100 times faster than
the off-line methods.
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2016 07:10:16 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Shao",
"Weixiang",
""
],
[
"He",
"Lifang",
""
],
[
"Lu",
"Chun-Ta",
""
],
[
"Wei",
"Xiaokai",
""
],
[
"Yu",
"Philip S.",
""
]
] | TITLE: Online Unsupervised Multi-view Feature Selection
ABSTRACT: In the era of big data, it is becoming common to have data with multiple
modalities or coming from multiple sources, known as "multi-view data".
Multi-view data are usually unlabeled and come from high-dimensional spaces
(such as language vocabularies), unsupervised multi-view feature selection is
crucial to many applications. However, it is nontrivial due to the following
challenges. First, there are too many instances or the feature dimensionality
is too large. Thus, the data may not fit in memory. How to select useful
features with limited memory space? Second, how to select features from
streaming data and handles the concept drift? Third, how to leverage the
consistent and complementary information from different views to improve the
feature selection in the situation when the data are too big or come in as
streams? To the best of our knowledge, none of the previous works can solve all
the challenges simultaneously. In this paper, we propose an Online unsupervised
Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming
multi-view data in an online fashion. OMVFS embeds unsupervised feature
selection into a clustering algorithm via NMF with sparse learning. It further
incorporates the graph regularization to preserve the local structure
information and help select discriminative features. Instead of storing all the
historical data, OMVFS processes the multi-view data chunk by chunk and
aggregates all the necessary information into several small matrices. By using
the buffering technique, the proposed OMVFS can reduce the computational and
storage cost while taking advantage of the structure information. Furthermore,
OMVFS can capture the concept drifts in the data streams. Extensive experiments
on four real-world datasets show the effectiveness and efficiency of the
proposed OMVFS method. More importantly, OMVFS is about 100 times faster than
the off-line methods.
| no_new_dataset | 0.941331 |
1609.08313 | Jun Yang | Jun Yang, Zhenhua Tian | Unsupervised Co-segmentation of 3D Shapes via Functional Maps | 14 pages, 8figures | null | null | null | cs.GR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an unsupervised method for co-segmentation of a set of 3D shapes
from the same class with the aim of segmenting the input shapes into consistent
semantic parts and establishing their correspondence across the set. Starting
from meaningful pre-segmentation of all given shapes individually, we construct
the correspondence between same candidate parts and obtain the labels via
functional maps. And then, we use these labels to mark the input shapes and
obtain results of co-segmentation. The core of our algorithm is to seek for an
optimal correspondence between semantically similar parts through functional
maps and mark such shape parts. Experimental results on the benchmark datasets
show the efficiency of this method and comparable accuracy to the
state-of-the-art algorithms.
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2016 08:35:14 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Yang",
"Jun",
""
],
[
"Tian",
"Zhenhua",
""
]
] | TITLE: Unsupervised Co-segmentation of 3D Shapes via Functional Maps
ABSTRACT: We present an unsupervised method for co-segmentation of a set of 3D shapes
from the same class with the aim of segmenting the input shapes into consistent
semantic parts and establishing their correspondence across the set. Starting
from meaningful pre-segmentation of all given shapes individually, we construct
the correspondence between same candidate parts and obtain the labels via
functional maps. And then, we use these labels to mark the input shapes and
obtain results of co-segmentation. The core of our algorithm is to seek for an
optimal correspondence between semantically similar parts through functional
maps and mark such shape parts. Experimental results on the benchmark datasets
show the efficiency of this method and comparable accuracy to the
state-of-the-art algorithms.
| no_new_dataset | 0.955068 |
1609.08399 | Mohamed Moustafa | Eman Ahmed, Mohamed Moustafa | House price estimation from visual and textual features | NCTA 2016. Final paper is on SCITEPRESS digital library | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most existing automatic house price estimation systems rely only on some
textual data like its neighborhood area and the number of rooms. The final
price is estimated by a human agent who visits the house and assesses it
visually. In this paper, we propose extracting visual features from house
photographs and combining them with the house's textual information. The
combined features are fed to a fully connected multilayer Neural Network (NN)
that estimates the house price as its single output. To train and evaluate our
network, we have collected the first houses dataset (to our knowledge) that
combines both images and textual attributes. The dataset is composed of 535
sample houses from the state of California, USA. Our experiments showed that
adding the visual features increased the R-value by a factor of 3 and decreased
the Mean Square Error (MSE) by one order of magnitude compared with
textual-only features. Additionally, when trained on the benchmark textual-only
features housing dataset, our proposed NN still outperformed the existing model
published results.
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2016 13:15:31 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Ahmed",
"Eman",
""
],
[
"Moustafa",
"Mohamed",
""
]
] | TITLE: House price estimation from visual and textual features
ABSTRACT: Most existing automatic house price estimation systems rely only on some
textual data like its neighborhood area and the number of rooms. The final
price is estimated by a human agent who visits the house and assesses it
visually. In this paper, we propose extracting visual features from house
photographs and combining them with the house's textual information. The
combined features are fed to a fully connected multilayer Neural Network (NN)
that estimates the house price as its single output. To train and evaluate our
network, we have collected the first houses dataset (to our knowledge) that
combines both images and textual attributes. The dataset is composed of 535
sample houses from the state of California, USA. Our experiments showed that
adding the visual features increased the R-value by a factor of 3 and decreased
the Mean Square Error (MSE) by one order of magnitude compared with
textual-only features. Additionally, when trained on the benchmark textual-only
features housing dataset, our proposed NN still outperformed the existing model
published results.
| new_dataset | 0.964656 |
1609.08409 | Giovanni Montana | Savelie Cornegruta, Robert Bakewell, Samuel Withey, Giovanni Montana | Modelling Radiological Language with Bidirectional Long Short-Term
Memory Networks | LOUHI 2016 conference proceedings | null | null | null | cs.CL stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivated by the need to automate medical information extraction from
free-text radiological reports, we present a bi-directional long short-term
memory (BiLSTM) neural network architecture for modelling radiological
language. The model has been used to address two NLP tasks: medical
named-entity recognition (NER) and negation detection. We investigate whether
learning several types of word embeddings improves BiLSTM's performance on
those tasks. Using a large dataset of chest x-ray reports, we compare the
proposed model to a baseline dictionary-based NER system and a negation
detection system that leverages the hand-crafted rules of the NegEx algorithm
and the grammatical relations obtained from the Stanford Dependency Parser.
Compared to these more traditional rule-based systems, we argue that BiLSTM
offers a strong alternative for both our tasks.
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2016 13:25:10 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Cornegruta",
"Savelie",
""
],
[
"Bakewell",
"Robert",
""
],
[
"Withey",
"Samuel",
""
],
[
"Montana",
"Giovanni",
""
]
] | TITLE: Modelling Radiological Language with Bidirectional Long Short-Term
Memory Networks
ABSTRACT: Motivated by the need to automate medical information extraction from
free-text radiological reports, we present a bi-directional long short-term
memory (BiLSTM) neural network architecture for modelling radiological
language. The model has been used to address two NLP tasks: medical
named-entity recognition (NER) and negation detection. We investigate whether
learning several types of word embeddings improves BiLSTM's performance on
those tasks. Using a large dataset of chest x-ray reports, we compare the
proposed model to a baseline dictionary-based NER system and a negation
detection system that leverages the hand-crafted rules of the NegEx algorithm
and the grammatical relations obtained from the Stanford Dependency Parser.
Compared to these more traditional rule-based systems, we argue that BiLSTM
offers a strong alternative for both our tasks.
| no_new_dataset | 0.944791 |
1609.08496 | Jipeng Qiang | Jipeng Qiang, Ping Chen, Tong Wang, Xindong Wu | Topic Modeling over Short Texts by Incorporating Word Embeddings | null | null | null | null | cs.CL cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inferring topics from the overwhelming amount of short texts becomes a
critical but challenging task for many content analysis tasks, such as content
charactering, user interest profiling, and emerging topic detecting. Existing
methods such as probabilistic latent semantic analysis (PLSA) and latent
Dirichlet allocation (LDA) cannot solve this prob- lem very well since only
very limited word co-occurrence information is available in short texts. This
paper studies how to incorporate the external word correlation knowledge into
short texts to improve the coherence of topic modeling. Based on recent results
in word embeddings that learn se- mantically representations for words from a
large corpus, we introduce a novel method, Embedding-based Topic Model (ETM),
to learn latent topics from short texts. ETM not only solves the problem of
very limited word co-occurrence information by aggregating short texts into
long pseudo- texts, but also utilizes a Markov Random Field regularized model
that gives correlated words a better chance to be put into the same topic. The
experiments on real-world datasets validate the effectiveness of our model
comparing with the state-of-the-art models.
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2016 15:26:07 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Qiang",
"Jipeng",
""
],
[
"Chen",
"Ping",
""
],
[
"Wang",
"Tong",
""
],
[
"Wu",
"Xindong",
""
]
] | TITLE: Topic Modeling over Short Texts by Incorporating Word Embeddings
ABSTRACT: Inferring topics from the overwhelming amount of short texts becomes a
critical but challenging task for many content analysis tasks, such as content
charactering, user interest profiling, and emerging topic detecting. Existing
methods such as probabilistic latent semantic analysis (PLSA) and latent
Dirichlet allocation (LDA) cannot solve this prob- lem very well since only
very limited word co-occurrence information is available in short texts. This
paper studies how to incorporate the external word correlation knowledge into
short texts to improve the coherence of topic modeling. Based on recent results
in word embeddings that learn se- mantically representations for words from a
large corpus, we introduce a novel method, Embedding-based Topic Model (ETM),
to learn latent topics from short texts. ETM not only solves the problem of
very limited word co-occurrence information by aggregating short texts into
long pseudo- texts, but also utilizes a Markov Random Field regularized model
that gives correlated words a better chance to be put into the same topic. The
experiments on real-world datasets validate the effectiveness of our model
comparing with the state-of-the-art models.
| no_new_dataset | 0.94801 |
1609.08535 | Peter Polack Jr | Peter J Polack Jr, Shang-Tse Chen, Minsuk Kahng, Kaya de Barbaro,
Moushumi Sharmin, Rahul Basole, Duen Horng Chau | Chronodes: Interactive Multi-focus Exploration of Event Sequences | null | null | null | null | cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The advent of mobile health technologies presents new challenges that
existing visualizations, interactive tools, and algorithms are not yet designed
to support. In dealing with uncertainty in sensor data and high-dimensional
physiological records, we must seek to improve current tools that make sense of
health data from traditional perspectives in event-based trend discovery. With
Chronodes, a system developed to help researchers collect, interpret, and model
mobile health (mHealth) data, we posit a series of interaction techniques that
enable new approaches to understanding and exploring event-based data. From
numerous and discontinuous mobile health data streams, Chronodes finds and
visualizes frequent event sequences that reveal common chronological patterns
across participants and days. By then promoting the sequences as interactive
elements, Chronodes presents opportunities for finding, defining, and comparing
cohorts of participants that exhibit particular behaviors. We applied Chronodes
to a real 40GB mHealth dataset capturing about 400 hours of data. Through our
pilot study with 20 behavioral and biomedical health experts, we gained
insights into Chronodes' efficacy, limitations, and potential applicability to
a wide range of healthcare scenarios.
| [
{
"version": "v1",
"created": "Tue, 27 Sep 2016 17:05:15 GMT"
}
] | 2016-09-28T00:00:00 | [
[
"Polack",
"Peter J",
"Jr"
],
[
"Chen",
"Shang-Tse",
""
],
[
"Kahng",
"Minsuk",
""
],
[
"de Barbaro",
"Kaya",
""
],
[
"Sharmin",
"Moushumi",
""
],
[
"Basole",
"Rahul",
""
],
[
"Chau",
"Duen Horng",
""
]
] | TITLE: Chronodes: Interactive Multi-focus Exploration of Event Sequences
ABSTRACT: The advent of mobile health technologies presents new challenges that
existing visualizations, interactive tools, and algorithms are not yet designed
to support. In dealing with uncertainty in sensor data and high-dimensional
physiological records, we must seek to improve current tools that make sense of
health data from traditional perspectives in event-based trend discovery. With
Chronodes, a system developed to help researchers collect, interpret, and model
mobile health (mHealth) data, we posit a series of interaction techniques that
enable new approaches to understanding and exploring event-based data. From
numerous and discontinuous mobile health data streams, Chronodes finds and
visualizes frequent event sequences that reveal common chronological patterns
across participants and days. By then promoting the sequences as interactive
elements, Chronodes presents opportunities for finding, defining, and comparing
cohorts of participants that exhibit particular behaviors. We applied Chronodes
to a real 40GB mHealth dataset capturing about 400 hours of data. Through our
pilot study with 20 behavioral and biomedical health experts, we gained
insights into Chronodes' efficacy, limitations, and potential applicability to
a wide range of healthcare scenarios.
| no_new_dataset | 0.942454 |
1504.00241 | Alex Borges Vieira | Eduardo Chinelate Costa and Alex Borges Vieira and Klaus Wehmuth and
Artur Ziviani and Ana Paula Couto da Silva | Time Centrality in Dynamic Complex Networks | null | Advances in Complex Systems (ACS), vol. 18, no. 07n08, November &
December 2015 | 10.1142/S021952591550023X | null | cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is an ever-increasing interest in investigating dynamics in
time-varying graphs (TVGs). Nevertheless, so far, the notion of centrality in
TVG scenarios usually refers to metrics that assess the relative importance of
nodes along the temporal evolution of the dynamic complex network. For some TVG
scenarios, however, more important than identifying the central nodes under a
given node centrality definition is identifying the key time instants for
taking certain actions. In this paper, we thus introduce and investigate the
notion of time centrality in TVGs. Analogously to node centrality, time
centrality evaluates the relative importance of time instants in dynamic
complex networks. In this context, we present two time centrality metrics
related to diffusion processes. We evaluate the two defined metrics using both
a real-world dataset representing an in-person contact dynamic network and a
synthetically generated randomized TVG. We validate the concept of time
centrality showing that diffusion starting at the best classified time instants
(i.e. the most central ones), according to our metrics, can perform a faster
and more efficient diffusion process.
| [
{
"version": "v1",
"created": "Wed, 1 Apr 2015 14:16:10 GMT"
},
{
"version": "v2",
"created": "Wed, 15 Apr 2015 15:13:09 GMT"
},
{
"version": "v3",
"created": "Sun, 6 Sep 2015 02:20:53 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Costa",
"Eduardo Chinelate",
""
],
[
"Vieira",
"Alex Borges",
""
],
[
"Wehmuth",
"Klaus",
""
],
[
"Ziviani",
"Artur",
""
],
[
"da Silva",
"Ana Paula Couto",
""
]
] | TITLE: Time Centrality in Dynamic Complex Networks
ABSTRACT: There is an ever-increasing interest in investigating dynamics in
time-varying graphs (TVGs). Nevertheless, so far, the notion of centrality in
TVG scenarios usually refers to metrics that assess the relative importance of
nodes along the temporal evolution of the dynamic complex network. For some TVG
scenarios, however, more important than identifying the central nodes under a
given node centrality definition is identifying the key time instants for
taking certain actions. In this paper, we thus introduce and investigate the
notion of time centrality in TVGs. Analogously to node centrality, time
centrality evaluates the relative importance of time instants in dynamic
complex networks. In this context, we present two time centrality metrics
related to diffusion processes. We evaluate the two defined metrics using both
a real-world dataset representing an in-person contact dynamic network and a
synthetically generated randomized TVG. We validate the concept of time
centrality showing that diffusion starting at the best classified time instants
(i.e. the most central ones), according to our metrics, can perform a faster
and more efficient diffusion process.
| no_new_dataset | 0.819821 |
1506.04304 | Jeremy Maitin-Shepard | Jeremy Maitin-Shepard (1 and 2), Viren Jain (2), Michal Januszewski
(2), Peter Li (2), Pieter Abbeel (1) ((1) UC Berkeley, (2) Google) | Combinatorial Energy Learning for Image Segmentation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new machine learning approach for image segmentation that uses
a neural network to model the conditional energy of a segmentation given an
image. Our approach, combinatorial energy learning for image segmentation
(CELIS) places a particular emphasis on modeling the inherent combinatorial
nature of dense image segmentation problems. We propose efficient algorithms
for learning deep neural networks to model the energy function, and for local
optimization of this energy in the space of supervoxel agglomerations. We
extensively evaluate our method on a publicly available 3-D microscopy dataset
with 25 billion voxels of ground truth data. On an 11 billion voxel test set,
we find that our method improves volumetric reconstruction accuracy by more
than 20% as compared to two state-of-the-art baseline methods: graph-based
segmentation of the output of a 3-D convolutional neural network trained to
predict boundaries, as well as a random forest classifier trained to
agglomerate supervoxels that were generated by a 3-D convolutional neural
network.
| [
{
"version": "v1",
"created": "Sat, 13 Jun 2015 18:23:42 GMT"
},
{
"version": "v2",
"created": "Tue, 16 Jun 2015 19:33:20 GMT"
},
{
"version": "v3",
"created": "Fri, 23 Sep 2016 20:47:55 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Maitin-Shepard",
"Jeremy",
"",
"1 and 2"
],
[
"Jain",
"Viren",
"",
"Google"
],
[
"Januszewski",
"Michal",
"",
"Google"
],
[
"Li",
"Peter",
"",
"Google"
],
[
"Abbeel",
"Pieter",
"",
"UC Berkeley"
]
] | TITLE: Combinatorial Energy Learning for Image Segmentation
ABSTRACT: We introduce a new machine learning approach for image segmentation that uses
a neural network to model the conditional energy of a segmentation given an
image. Our approach, combinatorial energy learning for image segmentation
(CELIS) places a particular emphasis on modeling the inherent combinatorial
nature of dense image segmentation problems. We propose efficient algorithms
for learning deep neural networks to model the energy function, and for local
optimization of this energy in the space of supervoxel agglomerations. We
extensively evaluate our method on a publicly available 3-D microscopy dataset
with 25 billion voxels of ground truth data. On an 11 billion voxel test set,
we find that our method improves volumetric reconstruction accuracy by more
than 20% as compared to two state-of-the-art baseline methods: graph-based
segmentation of the output of a 3-D convolutional neural network trained to
predict boundaries, as well as a random forest classifier trained to
agglomerate supervoxels that were generated by a 3-D convolutional neural
network.
| no_new_dataset | 0.949529 |
1605.04469 | Ye Zhang | Ye Zhang, Iain Marshall, Byron C. Wallace | Rationale-Augmented Convolutional Neural Networks for Text
Classification | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new Convolutional Neural Network (CNN) model for text
classification that jointly exploits labels on documents and their component
sentences. Specifically, we consider scenarios in which annotators explicitly
mark sentences (or snippets) that support their overall document
categorization, i.e., they provide rationales. Our model exploits such
supervision via a hierarchical approach in which each document is represented
by a linear combination of the vector representations of its component
sentences. We propose a sentence-level convolutional model that estimates the
probability that a given sentence is a rationale, and we then scale the
contribution of each sentence to the aggregate document representation in
proportion to these estimates. Experiments on five classification datasets that
have document labels and associated rationales demonstrate that our approach
consistently outperforms strong baselines. Moreover, our model naturally
provides explanations for its predictions.
| [
{
"version": "v1",
"created": "Sat, 14 May 2016 21:30:57 GMT"
},
{
"version": "v2",
"created": "Sat, 21 May 2016 01:05:59 GMT"
},
{
"version": "v3",
"created": "Sat, 24 Sep 2016 16:35:57 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Zhang",
"Ye",
""
],
[
"Marshall",
"Iain",
""
],
[
"Wallace",
"Byron C.",
""
]
] | TITLE: Rationale-Augmented Convolutional Neural Networks for Text
Classification
ABSTRACT: We present a new Convolutional Neural Network (CNN) model for text
classification that jointly exploits labels on documents and their component
sentences. Specifically, we consider scenarios in which annotators explicitly
mark sentences (or snippets) that support their overall document
categorization, i.e., they provide rationales. Our model exploits such
supervision via a hierarchical approach in which each document is represented
by a linear combination of the vector representations of its component
sentences. We propose a sentence-level convolutional model that estimates the
probability that a given sentence is a rationale, and we then scale the
contribution of each sentence to the aggregate document representation in
proportion to these estimates. Experiments on five classification datasets that
have document labels and associated rationales demonstrate that our approach
consistently outperforms strong baselines. Moreover, our model naturally
provides explanations for its predictions.
| no_new_dataset | 0.949059 |
1605.04502 | Bing Wang | Bing Wang, Li Wang, Bing Shuai, Zhen Zuo, Ting Liu, Kap Luk Chan, Gang
Wang | Joint Learning of Siamese CNNs and Temporally Constrained Metrics for
Tracklet Association | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study the challenging problem of multi-object tracking in a
complex scene captured by a single camera. Different from the existing tracklet
association-based tracking methods, we propose a novel and efficient way to
obtain discriminative appearance-based tracklet affinity models. Our proposed
method jointly learns the convolutional neural networks (CNNs) and temporally
constrained metrics. In our method, a Siamese convolutional neural network
(CNN) is first pre-trained on the auxiliary data. Then the Siamese CNN and
temporally constrained metrics are jointly learned online to construct the
appearance-based tracklet affinity models. The proposed method can jointly
learn the hierarchical deep features and temporally constrained segment-wise
metrics under a unified framework. For reliable association between tracklets,
a novel loss function incorporating temporally constrained multi-task learning
mechanism is proposed. By employing the proposed method, tracklet association
can be accomplished even in challenging situations. Moreover, a new dataset
with 40 fully annotated sequences is created to facilitate the tracking
evaluation. Experimental results on five public datasets and the new
large-scale dataset show that our method outperforms several state-of-the-art
approaches in multi-object tracking.
| [
{
"version": "v1",
"created": "Sun, 15 May 2016 07:09:28 GMT"
},
{
"version": "v2",
"created": "Sun, 25 Sep 2016 09:58:32 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Wang",
"Bing",
""
],
[
"Wang",
"Li",
""
],
[
"Shuai",
"Bing",
""
],
[
"Zuo",
"Zhen",
""
],
[
"Liu",
"Ting",
""
],
[
"Chan",
"Kap Luk",
""
],
[
"Wang",
"Gang",
""
]
] | TITLE: Joint Learning of Siamese CNNs and Temporally Constrained Metrics for
Tracklet Association
ABSTRACT: In this paper, we study the challenging problem of multi-object tracking in a
complex scene captured by a single camera. Different from the existing tracklet
association-based tracking methods, we propose a novel and efficient way to
obtain discriminative appearance-based tracklet affinity models. Our proposed
method jointly learns the convolutional neural networks (CNNs) and temporally
constrained metrics. In our method, a Siamese convolutional neural network
(CNN) is first pre-trained on the auxiliary data. Then the Siamese CNN and
temporally constrained metrics are jointly learned online to construct the
appearance-based tracklet affinity models. The proposed method can jointly
learn the hierarchical deep features and temporally constrained segment-wise
metrics under a unified framework. For reliable association between tracklets,
a novel loss function incorporating temporally constrained multi-task learning
mechanism is proposed. By employing the proposed method, tracklet association
can be accomplished even in challenging situations. Moreover, a new dataset
with 40 fully annotated sequences is created to facilitate the tracking
evaluation. Experimental results on five public datasets and the new
large-scale dataset show that our method outperforms several state-of-the-art
approaches in multi-object tracking.
| new_dataset | 0.960435 |
1605.08900 | Duyu Tang | Duyu Tang, Bing Qin, Ting Liu | Aspect Level Sentiment Classification with Deep Memory Network | published in EMNLP 2016 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a deep memory network for aspect level sentiment classification.
Unlike feature-based SVM and sequential neural models such as LSTM, this
approach explicitly captures the importance of each context word when inferring
the sentiment polarity of an aspect. Such importance degree and text
representation are calculated with multiple computational layers, each of which
is a neural attention model over an external memory. Experiments on laptop and
restaurant datasets demonstrate that our approach performs comparable to
state-of-art feature based SVM system, and substantially better than LSTM and
attention-based LSTM architectures. On both datasets we show that multiple
computational layers could improve the performance. Moreover, our approach is
also fast. The deep memory network with 9 layers is 15 times faster than LSTM
with a CPU implementation.
| [
{
"version": "v1",
"created": "Sat, 28 May 2016 14:47:49 GMT"
},
{
"version": "v2",
"created": "Sat, 24 Sep 2016 06:04:15 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Tang",
"Duyu",
""
],
[
"Qin",
"Bing",
""
],
[
"Liu",
"Ting",
""
]
] | TITLE: Aspect Level Sentiment Classification with Deep Memory Network
ABSTRACT: We introduce a deep memory network for aspect level sentiment classification.
Unlike feature-based SVM and sequential neural models such as LSTM, this
approach explicitly captures the importance of each context word when inferring
the sentiment polarity of an aspect. Such importance degree and text
representation are calculated with multiple computational layers, each of which
is a neural attention model over an external memory. Experiments on laptop and
restaurant datasets demonstrate that our approach performs comparable to
state-of-art feature based SVM system, and substantially better than LSTM and
attention-based LSTM architectures. On both datasets we show that multiple
computational layers could improve the performance. Moreover, our approach is
also fast. The deep memory network with 9 layers is 15 times faster than LSTM
with a CPU implementation.
| no_new_dataset | 0.949902 |
1606.01847 | Marcus Rohrbach | Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor
Darrell, and Marcus Rohrbach | Multimodal Compact Bilinear Pooling for Visual Question Answering and
Visual Grounding | Accepted to EMNLP 2016 | null | null | null | cs.CV cs.AI cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modeling textual or visual information with vector representations trained
from large language or visual datasets has been successfully explored in recent
years. However, tasks such as visual question answering require combining these
vector representations with each other. Approaches to multimodal pooling
include element-wise product or sum, as well as concatenation of the visual and
textual representations. We hypothesize that these methods are not as
expressive as an outer product of the visual and textual vectors. As the outer
product is typically infeasible due to its high dimensionality, we instead
propose utilizing Multimodal Compact Bilinear pooling (MCB) to efficiently and
expressively combine multimodal features. We extensively evaluate MCB on the
visual question answering and grounding tasks. We consistently show the benefit
of MCB over ablations without MCB. For visual question answering, we present an
architecture which uses MCB twice, once for predicting attention over spatial
features and again to combine the attended representation with the question
representation. This model outperforms the state-of-the-art on the Visual7W
dataset and the VQA challenge.
| [
{
"version": "v1",
"created": "Mon, 6 Jun 2016 17:59:56 GMT"
},
{
"version": "v2",
"created": "Thu, 23 Jun 2016 19:52:41 GMT"
},
{
"version": "v3",
"created": "Sat, 24 Sep 2016 01:58:59 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Fukui",
"Akira",
""
],
[
"Park",
"Dong Huk",
""
],
[
"Yang",
"Daylen",
""
],
[
"Rohrbach",
"Anna",
""
],
[
"Darrell",
"Trevor",
""
],
[
"Rohrbach",
"Marcus",
""
]
] | TITLE: Multimodal Compact Bilinear Pooling for Visual Question Answering and
Visual Grounding
ABSTRACT: Modeling textual or visual information with vector representations trained
from large language or visual datasets has been successfully explored in recent
years. However, tasks such as visual question answering require combining these
vector representations with each other. Approaches to multimodal pooling
include element-wise product or sum, as well as concatenation of the visual and
textual representations. We hypothesize that these methods are not as
expressive as an outer product of the visual and textual vectors. As the outer
product is typically infeasible due to its high dimensionality, we instead
propose utilizing Multimodal Compact Bilinear pooling (MCB) to efficiently and
expressively combine multimodal features. We extensively evaluate MCB on the
visual question answering and grounding tasks. We consistently show the benefit
of MCB over ablations without MCB. For visual question answering, we present an
architecture which uses MCB twice, once for predicting attention over spatial
features and again to combine the attended representation with the question
representation. This model outperforms the state-of-the-art on the Visual7W
dataset and the VQA challenge.
| no_new_dataset | 0.943919 |
1606.01933 | Ankur Parikh | Ankur P. Parikh, Oscar T\"ackstr\"om, Dipanjan Das, Jakob Uszkoreit | A Decomposable Attention Model for Natural Language Inference | 7 pages, 1 figure, Proceeedings of EMNLP 2016 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a simple neural architecture for natural language inference. Our
approach uses attention to decompose the problem into subproblems that can be
solved separately, thus making it trivially parallelizable. On the Stanford
Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results
with almost an order of magnitude fewer parameters than previous work and
without relying on any word-order information. Adding intra-sentence attention
that takes a minimum amount of order into account yields further improvements.
| [
{
"version": "v1",
"created": "Mon, 6 Jun 2016 20:30:57 GMT"
},
{
"version": "v2",
"created": "Sun, 25 Sep 2016 23:52:45 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Parikh",
"Ankur P.",
""
],
[
"Täckström",
"Oscar",
""
],
[
"Das",
"Dipanjan",
""
],
[
"Uszkoreit",
"Jakob",
""
]
] | TITLE: A Decomposable Attention Model for Natural Language Inference
ABSTRACT: We propose a simple neural architecture for natural language inference. Our
approach uses attention to decompose the problem into subproblems that can be
solved separately, thus making it trivially parallelizable. On the Stanford
Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results
with almost an order of magnitude fewer parameters than previous work and
without relying on any word-order information. Adding intra-sentence attention
that takes a minimum amount of order into account yields further improvements.
| no_new_dataset | 0.951504 |
1607.08378 | Rahul Rama Varior Mr. | Rahul Rama Varior, Mrinal Haloi, and Gang Wang | Gated Siamese Convolutional Neural Network Architecture for Human
Re-Identification | Accepted to ECCV2016 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Matching pedestrians across multiple camera views, known as human
re-identification, is a challenging research problem that has numerous
applications in visual surveillance. With the resurgence of Convolutional
Neural Networks (CNNs), several end-to-end deep Siamese CNN architectures have
been proposed for human re-identification with the objective of projecting the
images of similar pairs (i.e. same identity) to be closer to each other and
those of dissimilar pairs to be distant from each other. However, current
networks extract fixed representations for each image regardless of other
images which are paired with it and the comparison with other images is done
only at the final level. In this setting, the network is at risk of failing to
extract finer local patterns that may be essential to distinguish positive
pairs from hard negative pairs. In this paper, we propose a gating function to
selectively emphasize such fine common local patterns by comparing the
mid-level features across pairs of images. This produces flexible
representations for the same image according to the images they are paired
with. We conduct experiments on the CUHK03, Market-1501 and VIPeR datasets and
demonstrate improved performance compared to a baseline Siamese CNN
architecture.
| [
{
"version": "v1",
"created": "Thu, 28 Jul 2016 09:40:18 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Sep 2016 16:28:58 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Varior",
"Rahul Rama",
""
],
[
"Haloi",
"Mrinal",
""
],
[
"Wang",
"Gang",
""
]
] | TITLE: Gated Siamese Convolutional Neural Network Architecture for Human
Re-Identification
ABSTRACT: Matching pedestrians across multiple camera views, known as human
re-identification, is a challenging research problem that has numerous
applications in visual surveillance. With the resurgence of Convolutional
Neural Networks (CNNs), several end-to-end deep Siamese CNN architectures have
been proposed for human re-identification with the objective of projecting the
images of similar pairs (i.e. same identity) to be closer to each other and
those of dissimilar pairs to be distant from each other. However, current
networks extract fixed representations for each image regardless of other
images which are paired with it and the comparison with other images is done
only at the final level. In this setting, the network is at risk of failing to
extract finer local patterns that may be essential to distinguish positive
pairs from hard negative pairs. In this paper, we propose a gating function to
selectively emphasize such fine common local patterns by comparing the
mid-level features across pairs of images. This produces flexible
representations for the same image according to the images they are paired
with. We conduct experiments on the CUHK03, Market-1501 and VIPeR datasets and
demonstrate improved performance compared to a baseline Siamese CNN
architecture.
| no_new_dataset | 0.951323 |
1609.04387 | Elad Richardson | Elad Richardson, Matan Sela, Ron Kimmel | 3D Face Reconstruction by Learning from Synthetic Data | The first two authors contributed equally to this work | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fast and robust three-dimensional reconstruction of facial geometric
structure from a single image is a challenging task with numerous applications.
Here, we introduce a learning-based approach for reconstructing a
three-dimensional face from a single image. Recent face recovery methods rely
on accurate localization of key characteristic points. In contrast, the
proposed approach is based on a Convolutional-Neural-Network (CNN) which
extracts the face geometry directly from its image. Although such deep
architectures outperform other models in complex computer vision problems,
training them properly requires a large dataset of annotated examples. In the
case of three-dimensional faces, currently, there are no large volume data
sets, while acquiring such big-data is a tedious task. As an alternative, we
propose to generate random, yet nearly photo-realistic, facial images for which
the geometric form is known. The suggested model successfully recovers facial
shapes from real images, even for faces with extreme expressions and under
various lighting conditions.
| [
{
"version": "v1",
"created": "Wed, 14 Sep 2016 19:47:12 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Sep 2016 12:12:34 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Richardson",
"Elad",
""
],
[
"Sela",
"Matan",
""
],
[
"Kimmel",
"Ron",
""
]
] | TITLE: 3D Face Reconstruction by Learning from Synthetic Data
ABSTRACT: Fast and robust three-dimensional reconstruction of facial geometric
structure from a single image is a challenging task with numerous applications.
Here, we introduce a learning-based approach for reconstructing a
three-dimensional face from a single image. Recent face recovery methods rely
on accurate localization of key characteristic points. In contrast, the
proposed approach is based on a Convolutional-Neural-Network (CNN) which
extracts the face geometry directly from its image. Although such deep
architectures outperform other models in complex computer vision problems,
training them properly requires a large dataset of annotated examples. In the
case of three-dimensional faces, currently, there are no large volume data
sets, while acquiring such big-data is a tedious task. As an alternative, we
propose to generate random, yet nearly photo-realistic, facial images for which
the geometric form is known. The suggested model successfully recovers facial
shapes from real images, even for faces with extreme expressions and under
various lighting conditions.
| no_new_dataset | 0.945197 |
1609.07480 | Stylianos Kampakis | Stylianos Kampakis | Predictive modelling of football injuries | PhD Thesis submitted and defended successfully at the Department of
Computer Science at University College London | null | null | null | stat.AP cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The goal of this thesis is to investigate the potential of predictive
modelling for football injuries. This work was conducted in close collaboration
with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation
of Wolverhampton Wanderers (WW).
Three investigations were conducted:
1. Predicting the recovery time of football injuries using the UEFA injury
recordings: The UEFA recordings is a common standard for recording injuries in
professional football. For this investigation, three datasets of UEFA injury
recordings were available. Different machine learning algorithms were used in
order to build a predictive model. The performance of the machine learning
models is then improved by using feature selection conducted through
correlation-based subset feature selection and random forests.
2. Predicting injuries in professional football using exposure records: The
relationship between exposure (in training hours and match hours) in
professional football athletes and injury incidence was studied. A common
problem in football is understanding how the training schedule of an athlete
can affect the chance of him getting injured. The task was to predict the
number of days a player can train before he gets injured.
3. Predicting intrinsic injury incidence using in-training GPS measurements:
A significant percentage of football injuries can be attributed to overtraining
and fatigue. GPS data collected during training sessions might provide
indicators of fatigue, or might be used to detect very intense training
sessions which can lead to overtraining. This research used GPS data gathered
during training sessions of the first team of THFC, in order to predict whether
an injury would take place during a week.
| [
{
"version": "v1",
"created": "Tue, 20 Sep 2016 11:58:42 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Kampakis",
"Stylianos",
""
]
] | TITLE: Predictive modelling of football injuries
ABSTRACT: The goal of this thesis is to investigate the potential of predictive
modelling for football injuries. This work was conducted in close collaboration
with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation
of Wolverhampton Wanderers (WW).
Three investigations were conducted:
1. Predicting the recovery time of football injuries using the UEFA injury
recordings: The UEFA recordings is a common standard for recording injuries in
professional football. For this investigation, three datasets of UEFA injury
recordings were available. Different machine learning algorithms were used in
order to build a predictive model. The performance of the machine learning
models is then improved by using feature selection conducted through
correlation-based subset feature selection and random forests.
2. Predicting injuries in professional football using exposure records: The
relationship between exposure (in training hours and match hours) in
professional football athletes and injury incidence was studied. A common
problem in football is understanding how the training schedule of an athlete
can affect the chance of him getting injured. The task was to predict the
number of days a player can train before he gets injured.
3. Predicting intrinsic injury incidence using in-training GPS measurements:
A significant percentage of football injuries can be attributed to overtraining
and fatigue. GPS data collected during training sessions might provide
indicators of fatigue, or might be used to detect very intense training
sessions which can lead to overtraining. This research used GPS data gathered
during training sessions of the first team of THFC, in order to predict whether
an injury would take place during a week.
| no_new_dataset | 0.931338 |
1609.07495 | Matteo Ruggero Ronchi | Matteo Ruggero Ronchi, Joon Sik Kim and Yisong Yue | A Rotation Invariant Latent Factor Model for Moveme Discovery from
Static Poses | Long version of the paper accepted at the IEEE ICDM 2016 conference.
10 pages, 9 figures, 1 table. Project page:
http://www.vision.caltech.edu/~mronchi/projects/RotationInvariantMovemes/ | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | We tackle the problem of learning a rotation invariant latent factor model
when the training data is comprised of lower-dimensional projections of the
original feature space. The main goal is the discovery of a set of 3-D bases
poses that can characterize the manifold of primitive human motions, or
movemes, from a training set of 2-D projected poses obtained from still images
taken at various camera angles. The proposed technique for basis discovery is
data-driven rather than hand-designed. The learned representation is rotation
invariant, and can reconstruct any training instance from multiple viewing
angles. We apply our method to modeling human poses in sports (via the Leeds
Sports Dataset), and demonstrate the effectiveness of the learned bases in a
range of applications such as activity classification, inference of dynamics
from a single frame, and synthetic representation of movements.
| [
{
"version": "v1",
"created": "Fri, 23 Sep 2016 20:00:23 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Ronchi",
"Matteo Ruggero",
""
],
[
"Kim",
"Joon Sik",
""
],
[
"Yue",
"Yisong",
""
]
] | TITLE: A Rotation Invariant Latent Factor Model for Moveme Discovery from
Static Poses
ABSTRACT: We tackle the problem of learning a rotation invariant latent factor model
when the training data is comprised of lower-dimensional projections of the
original feature space. The main goal is the discovery of a set of 3-D bases
poses that can characterize the manifold of primitive human motions, or
movemes, from a training set of 2-D projected poses obtained from still images
taken at various camera angles. The proposed technique for basis discovery is
data-driven rather than hand-designed. The learned representation is rotation
invariant, and can reconstruct any training instance from multiple viewing
angles. We apply our method to modeling human poses in sports (via the Leeds
Sports Dataset), and demonstrate the effectiveness of the learned bases in a
range of applications such as activity classification, inference of dynamics
from a single frame, and synthetic representation of movements.
| no_new_dataset | 0.947721 |
1609.07569 | Aming Li | Aming Li, Lei Zhou, Qi Su, Sean P. Cornelius, Yang-Yu Liu, Long Wang | Evolution of Cooperation on Temporal Networks | 23 pages, 12 figures | null | null | null | physics.soc-ph physics.bio-ph q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The structure of social networks is a key determinant in fostering
cooperation and other altruistic behavior among naturally selfish individuals.
However, most real social interactions are temporal, being both finite in
duration and spread out over time. This raises the question of whether stable
cooperation can form despite an intrinsically fragmented social fabric. Here we
develop a framework to study the evolution of cooperation on temporal networks
in the setting of the classic Prisoner's Dilemma. By analyzing both real and
synthetic datasets, we find that temporal networks generally facilitate the
evolution of cooperation compared to their static counterparts. More
interestingly, we find that the intrinsic human interactive pattern like bursty
behavior impedes the evolution of cooperation. Finally, we introduce a measure
to quantify the temporality present in networks and demonstrate that there is
an intermediate level of temporality that boosts cooperation most. Our results
open a new avenue for investigating the evolution of cooperation in more
realistic structured populations.
| [
{
"version": "v1",
"created": "Sat, 24 Sep 2016 04:18:25 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Li",
"Aming",
""
],
[
"Zhou",
"Lei",
""
],
[
"Su",
"Qi",
""
],
[
"Cornelius",
"Sean P.",
""
],
[
"Liu",
"Yang-Yu",
""
],
[
"Wang",
"Long",
""
]
] | TITLE: Evolution of Cooperation on Temporal Networks
ABSTRACT: The structure of social networks is a key determinant in fostering
cooperation and other altruistic behavior among naturally selfish individuals.
However, most real social interactions are temporal, being both finite in
duration and spread out over time. This raises the question of whether stable
cooperation can form despite an intrinsically fragmented social fabric. Here we
develop a framework to study the evolution of cooperation on temporal networks
in the setting of the classic Prisoner's Dilemma. By analyzing both real and
synthetic datasets, we find that temporal networks generally facilitate the
evolution of cooperation compared to their static counterparts. More
interestingly, we find that the intrinsic human interactive pattern like bursty
behavior impedes the evolution of cooperation. Finally, we introduce a measure
to quantify the temporality present in networks and demonstrate that there is
an intermediate level of temporality that boosts cooperation most. Our results
open a new avenue for investigating the evolution of cooperation in more
realistic structured populations.
| no_new_dataset | 0.94545 |
1609.07599 | Shenglan Liu | Shenglan Liu, Muxin Sun, Lin Feng, Yang Liu, Jun Wu | Three Tiers Neighborhood Graph and Multi-graph Fusion Ranking for
Multi-feature Image Retrieval: A Manifold Aspect | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Single feature is inefficient to describe content of an image, which is a
shortcoming in traditional image retrieval task. We know that one image can be
described by different features. Multi-feature fusion ranking can be utilized
to improve the ranking list of query. In this paper, we first analyze graph
structure and multi-feature fusion re-ranking from manifold aspect. Then, Three
Tiers Neighborhood Graph (TTNG) is constructed to re-rank the original ranking
list by single feature and to enhance precision of single feature. Furthermore,
we propose Multi-graph Fusion Ranking (MFR) for multi-feature ranking, which
considers the correlation of all images in multiple neighborhood graphs.
Evaluations are conducted on UK-bench, Corel-1K, Corel-10K and Cifar-10
benchmark datasets. The experimental results show that our TTNG and MFR
outperform than other state-of-the-art methods. For example, we achieve
competitive results N-S score 3.91 and precision 65.00% on UK-bench and
Corel-10K datasets respectively.
| [
{
"version": "v1",
"created": "Sat, 24 Sep 2016 10:34:36 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Liu",
"Shenglan",
""
],
[
"Sun",
"Muxin",
""
],
[
"Feng",
"Lin",
""
],
[
"Liu",
"Yang",
""
],
[
"Wu",
"Jun",
""
]
] | TITLE: Three Tiers Neighborhood Graph and Multi-graph Fusion Ranking for
Multi-feature Image Retrieval: A Manifold Aspect
ABSTRACT: Single feature is inefficient to describe content of an image, which is a
shortcoming in traditional image retrieval task. We know that one image can be
described by different features. Multi-feature fusion ranking can be utilized
to improve the ranking list of query. In this paper, we first analyze graph
structure and multi-feature fusion re-ranking from manifold aspect. Then, Three
Tiers Neighborhood Graph (TTNG) is constructed to re-rank the original ranking
list by single feature and to enhance precision of single feature. Furthermore,
we propose Multi-graph Fusion Ranking (MFR) for multi-feature ranking, which
considers the correlation of all images in multiple neighborhood graphs.
Evaluations are conducted on UK-bench, Corel-1K, Corel-10K and Cifar-10
benchmark datasets. The experimental results show that our TTNG and MFR
outperform than other state-of-the-art methods. For example, we achieve
competitive results N-S score 3.91 and precision 65.00% on UK-bench and
Corel-10K datasets respectively.
| no_new_dataset | 0.947914 |
1609.07603 | Claus Brenner | Claus Brenner | Scalable Estimation of Precision Maps in a MapReduce Framework | ACM SIGSPATIAL'16, October 31-November 03, 2016, Burlingame, CA, USA | null | 10.1145/2996913.2996990 | null | cs.DC cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a large-scale strip adjustment method for LiDAR mobile
mapping data, yielding highly precise maps. It uses several concepts to achieve
scalability. First, an efficient graph-based pre-segmentation is used, which
directly operates on LiDAR scan strip data, rather than on point clouds.
Second, observation equations are obtained from a dense matching, which is
formulated in terms of an estimation of a latent map. As a result of this
formulation, the number of observation equations is not quadratic, but rather
linear in the number of scan strips. Third, the dynamic Bayes network, which
results from all observation and condition equations, is partitioned into two
sub-networks. Consequently, the estimation matrices for all position and
orientation corrections are linear instead of quadratic in the number of
unknowns and can be solved very efficiently using an alternating least squares
approach. It is shown how this approach can be mapped to a standard key/value
MapReduce implementation, where each of the processing nodes operates
independently on small chunks of data, leading to essentially linear
scalability. Results are demonstrated for a dataset of one billion measured
LiDAR points and 278,000 unknowns, leading to maps with a precision of a few
millimeters.
| [
{
"version": "v1",
"created": "Sat, 24 Sep 2016 11:24:30 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Brenner",
"Claus",
""
]
] | TITLE: Scalable Estimation of Precision Maps in a MapReduce Framework
ABSTRACT: This paper presents a large-scale strip adjustment method for LiDAR mobile
mapping data, yielding highly precise maps. It uses several concepts to achieve
scalability. First, an efficient graph-based pre-segmentation is used, which
directly operates on LiDAR scan strip data, rather than on point clouds.
Second, observation equations are obtained from a dense matching, which is
formulated in terms of an estimation of a latent map. As a result of this
formulation, the number of observation equations is not quadratic, but rather
linear in the number of scan strips. Third, the dynamic Bayes network, which
results from all observation and condition equations, is partitioned into two
sub-networks. Consequently, the estimation matrices for all position and
orientation corrections are linear instead of quadratic in the number of
unknowns and can be solved very efficiently using an alternating least squares
approach. It is shown how this approach can be mapped to a standard key/value
MapReduce implementation, where each of the processing nodes operates
independently on small chunks of data, leading to essentially linear
scalability. Results are demonstrated for a dataset of one billion measured
LiDAR points and 278,000 unknowns, leading to maps with a precision of a few
millimeters.
| no_new_dataset | 0.938969 |
1609.07615 | Shenglan Liu | Shenglan Liu, Jun Wu, Lin Feng, Yang Liu, Hong Qiao, Wenbo Luo Muxin
Sun, and Wei Wang | Perceptual uniform descriptor and Ranking on manifold: A bridge between
image representation and ranking for image retrieval | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Incompatibility of image descriptor and ranking is always neglected in image
retrieval. In this paper, manifold learning and Gestalt psychology theory are
involved to solve the incompatibility problem. A new holistic descriptor called
Perceptual Uniform Descriptor (PUD) based on Gestalt psychology is proposed,
which combines color and gradient direction to imitate the human visual
uniformity. PUD features in the same class images distributes on one manifold
in most cases because PUD improves the visual uniformity of the traditional
descriptors. Thus, we use manifold ranking and PUD to realize image retrieval.
Experiments were carried out on five benchmark data sets, and the proposed
method can greatly improve the accuracy of image retrieval. Our experimental
results in the Ukbench and Corel-1K datasets demonstrated that N-S score
reached to 3.58 (HSV 3.4) and mAP to 81.77% (ODBTC 77.9%) respectively by
utilizing PUD which has only 280 dimension. The results are higher than other
holistic image descriptors (even some local ones) and state-of-the-arts
retrieval methods.
| [
{
"version": "v1",
"created": "Sat, 24 Sep 2016 13:13:38 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Liu",
"Shenglan",
""
],
[
"Wu",
"Jun",
""
],
[
"Feng",
"Lin",
""
],
[
"Liu",
"Yang",
""
],
[
"Qiao",
"Hong",
""
],
[
"Sun",
"Wenbo Luo Muxin",
""
],
[
"Wang",
"Wei",
""
]
] | TITLE: Perceptual uniform descriptor and Ranking on manifold: A bridge between
image representation and ranking for image retrieval
ABSTRACT: Incompatibility of image descriptor and ranking is always neglected in image
retrieval. In this paper, manifold learning and Gestalt psychology theory are
involved to solve the incompatibility problem. A new holistic descriptor called
Perceptual Uniform Descriptor (PUD) based on Gestalt psychology is proposed,
which combines color and gradient direction to imitate the human visual
uniformity. PUD features in the same class images distributes on one manifold
in most cases because PUD improves the visual uniformity of the traditional
descriptors. Thus, we use manifold ranking and PUD to realize image retrieval.
Experiments were carried out on five benchmark data sets, and the proposed
method can greatly improve the accuracy of image retrieval. Our experimental
results in the Ukbench and Corel-1K datasets demonstrated that N-S score
reached to 3.58 (HSV 3.4) and mAP to 81.77% (ODBTC 77.9%) respectively by
utilizing PUD which has only 280 dimension. The results are higher than other
holistic image descriptors (even some local ones) and state-of-the-arts
retrieval methods.
| no_new_dataset | 0.952175 |
1609.07826 | Georgios Georgakis | Georgios Georgakis, Md Alimoor Reza, Arsalan Mousavian, Phi-Hung Le,
Jana Kosecka | Multiview RGB-D Dataset for Object Instance Detection | null | null | null | null | cs.CV cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a new multi-view RGB-D dataset of nine kitchen scenes,
each containing several objects in realistic cluttered environments including a
subset of objects from the BigBird dataset. The viewpoints of the scenes are
densely sampled and objects in the scenes are annotated with bounding boxes and
in the 3D point cloud. Also, an approach for detection and recognition is
presented, which is comprised of two parts: i) a new multi-view 3D proposal
generation method and ii) the development of several recognition baselines
using AlexNet to score our proposals, which is trained either on crops of the
dataset or on synthetically composited training images. Finally, we compare the
performance of the object proposals and a detection baseline to the Washington
RGB-D Scenes (WRGB-D) dataset and demonstrate that our Kitchen scenes dataset
is more challenging for object detection and recognition. The dataset is
available at: http://cs.gmu.edu/~robot/gmu-kitchens.html.
| [
{
"version": "v1",
"created": "Mon, 26 Sep 2016 01:18:56 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Georgakis",
"Georgios",
""
],
[
"Reza",
"Md Alimoor",
""
],
[
"Mousavian",
"Arsalan",
""
],
[
"Le",
"Phi-Hung",
""
],
[
"Kosecka",
"Jana",
""
]
] | TITLE: Multiview RGB-D Dataset for Object Instance Detection
ABSTRACT: This paper presents a new multi-view RGB-D dataset of nine kitchen scenes,
each containing several objects in realistic cluttered environments including a
subset of objects from the BigBird dataset. The viewpoints of the scenes are
densely sampled and objects in the scenes are annotated with bounding boxes and
in the 3D point cloud. Also, an approach for detection and recognition is
presented, which is comprised of two parts: i) a new multi-view 3D proposal
generation method and ii) the development of several recognition baselines
using AlexNet to score our proposals, which is trained either on crops of the
dataset or on synthetically composited training images. Finally, we compare the
performance of the object proposals and a detection baseline to the Washington
RGB-D Scenes (WRGB-D) dataset and demonstrate that our Kitchen scenes dataset
is more challenging for object detection and recognition. The dataset is
available at: http://cs.gmu.edu/~robot/gmu-kitchens.html.
| new_dataset | 0.95877 |
1609.08084 | Yi Yang | Yi Yang, Ming-Wei Chang, Jacob Eisenstein | Toward Socially-Infused Information Extraction: Embedding Authors,
Mentions, and Entities | Accepted to EMNLP 2016 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Entity linking is the task of identifying mentions of entities in text, and
linking them to entries in a knowledge base. This task is especially difficult
in microblogs, as there is little additional text to provide disambiguating
context; rather, authors rely on an implicit common ground of shared knowledge
with their readers. In this paper, we attempt to capture some of this implicit
context by exploiting the social network structure in microblogs. We build on
the theory of homophily, which implies that socially linked individuals share
interests, and are therefore likely to mention the same sorts of entities. We
implement this idea by encoding authors, mentions, and entities in a continuous
vector space, which is constructed so that socially-connected authors have
similar vector representations. These vectors are incorporated into a neural
structured prediction model, which captures structural constraints that are
inherent in the entity linking task. Together, these design decisions yield F1
improvements of 1%-5% on benchmark datasets, as compared to the previous
state-of-the-art.
| [
{
"version": "v1",
"created": "Mon, 26 Sep 2016 17:19:07 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Yang",
"Yi",
""
],
[
"Chang",
"Ming-Wei",
""
],
[
"Eisenstein",
"Jacob",
""
]
] | TITLE: Toward Socially-Infused Information Extraction: Embedding Authors,
Mentions, and Entities
ABSTRACT: Entity linking is the task of identifying mentions of entities in text, and
linking them to entries in a knowledge base. This task is especially difficult
in microblogs, as there is little additional text to provide disambiguating
context; rather, authors rely on an implicit common ground of shared knowledge
with their readers. In this paper, we attempt to capture some of this implicit
context by exploiting the social network structure in microblogs. We build on
the theory of homophily, which implies that socially linked individuals share
interests, and are therefore likely to mention the same sorts of entities. We
implement this idea by encoding authors, mentions, and entities in a continuous
vector space, which is constructed so that socially-connected authors have
similar vector representations. These vectors are incorporated into a neural
structured prediction model, which captures structural constraints that are
inherent in the entity linking task. Together, these design decisions yield F1
improvements of 1%-5% on benchmark datasets, as compared to the previous
state-of-the-art.
| no_new_dataset | 0.947624 |
1609.08124 | Atousa Torabi Atousa Torabi | Atousa Torabi, Niket Tandon, Leonid Sigal | Learning Language-Visual Embedding for Movie Understanding with
Natural-Language | 13 pages | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning a joint language-visual embedding has a number of very appealing
properties and can result in variety of practical application, including
natural language image/video annotation and search. In this work, we study
three different joint language-visual neural network model architectures. We
evaluate our models on large scale LSMDC16 movie dataset for two tasks: 1)
Standard Ranking for video annotation and retrieval 2) Our proposed movie
multiple-choice test. This test facilitate automatic evaluation of
visual-language models for natural language video annotation based on human
activities. In addition to original Audio Description (AD) captions, provided
as part of LSMDC16, we collected and will make available a) manually generated
re-phrasings of those captions obtained using Amazon MTurk b) automatically
generated human activity elements in "Predicate + Object" (PO) phrases based on
"Knowlywood", an activity knowledge mining model. Our best model archives
Recall@10 of 19.2% on annotation and 18.9% on video retrieval tasks for subset
of 1000 samples. For multiple-choice test, our best model achieve accuracy
58.11% over whole LSMDC16 public test-set.
| [
{
"version": "v1",
"created": "Mon, 26 Sep 2016 19:14:12 GMT"
}
] | 2016-09-27T00:00:00 | [
[
"Torabi",
"Atousa",
""
],
[
"Tandon",
"Niket",
""
],
[
"Sigal",
"Leonid",
""
]
] | TITLE: Learning Language-Visual Embedding for Movie Understanding with
Natural-Language
ABSTRACT: Learning a joint language-visual embedding has a number of very appealing
properties and can result in variety of practical application, including
natural language image/video annotation and search. In this work, we study
three different joint language-visual neural network model architectures. We
evaluate our models on large scale LSMDC16 movie dataset for two tasks: 1)
Standard Ranking for video annotation and retrieval 2) Our proposed movie
multiple-choice test. This test facilitate automatic evaluation of
visual-language models for natural language video annotation based on human
activities. In addition to original Audio Description (AD) captions, provided
as part of LSMDC16, we collected and will make available a) manually generated
re-phrasings of those captions obtained using Amazon MTurk b) automatically
generated human activity elements in "Predicate + Object" (PO) phrases based on
"Knowlywood", an activity knowledge mining model. Our best model archives
Recall@10 of 19.2% on annotation and 18.9% on video retrieval tasks for subset
of 1000 samples. For multiple-choice test, our best model achieve accuracy
58.11% over whole LSMDC16 public test-set.
| no_new_dataset | 0.944228 |
1511.02930 | Vishesh Karwa | Vishesh Karwa and Pavel N. Krivitsky and Aleksandra B. Slavkovi\'c | Sharing Social Network Data: Differentially Private Estimation of
Exponential-Family Random Graph Models | Updated, 39 pages | null | null | null | stat.CO cs.CR cs.SI stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivated by a real-life problem of sharing social network data that contain
sensitive personal information, we propose a novel approach to release and
analyze synthetic graphs in order to protect privacy of individual
relationships captured by the social network while maintaining the validity of
statistical results. A case study using a version of the Enron e-mail corpus
dataset demonstrates the application and usefulness of the proposed techniques
in solving the challenging problem of maintaining privacy \emph{and} supporting
open access to network data to ensure reproducibility of existing studies and
discovering new scientific insights that can be obtained by analyzing such
data. We use a simple yet effective randomized response mechanism to generate
synthetic networks under $\epsilon$-edge differential privacy, and then use
likelihood based inference for missing data and Markov chain Monte Carlo
techniques to fit exponential-family random graph models to the generated
synthetic networks.
| [
{
"version": "v1",
"created": "Mon, 9 Nov 2015 23:36:30 GMT"
},
{
"version": "v2",
"created": "Fri, 23 Sep 2016 16:48:20 GMT"
}
] | 2016-09-26T00:00:00 | [
[
"Karwa",
"Vishesh",
""
],
[
"Krivitsky",
"Pavel N.",
""
],
[
"Slavković",
"Aleksandra B.",
""
]
] | TITLE: Sharing Social Network Data: Differentially Private Estimation of
Exponential-Family Random Graph Models
ABSTRACT: Motivated by a real-life problem of sharing social network data that contain
sensitive personal information, we propose a novel approach to release and
analyze synthetic graphs in order to protect privacy of individual
relationships captured by the social network while maintaining the validity of
statistical results. A case study using a version of the Enron e-mail corpus
dataset demonstrates the application and usefulness of the proposed techniques
in solving the challenging problem of maintaining privacy \emph{and} supporting
open access to network data to ensure reproducibility of existing studies and
discovering new scientific insights that can be obtained by analyzing such
data. We use a simple yet effective randomized response mechanism to generate
synthetic networks under $\epsilon$-edge differential privacy, and then use
likelihood based inference for missing data and Markov chain Monte Carlo
techniques to fit exponential-family random graph models to the generated
synthetic networks.
| no_new_dataset | 0.943138 |
1603.07771 | David Grangier | Remi Lebret, David Grangier, Michael Auli | Neural Text Generation from Structured Data with Application to the
Biography Domain | Conference on Empirical Methods in Natural Language Processing
(EMNLP), 2016 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces a neural model for concept-to-text generation that
scales to large, rich domains. We experiment with a new dataset of biographies
from Wikipedia that is an order of magnitude larger than existing resources
with over 700k samples. The dataset is also vastly more diverse with a 400k
vocabulary, compared to a few hundred words for Weathergov or Robocup. Our
model builds upon recent work on conditional neural language model for text
generation. To deal with the large vocabulary, we extend these models to mix a
fixed vocabulary with copy actions that transfer sample-specific words from the
input database to the generated output sentence. Our neural model significantly
out-performs a classical Kneser-Ney language model adapted to this task by
nearly 15 BLEU.
| [
{
"version": "v1",
"created": "Thu, 24 Mar 2016 22:40:00 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Sep 2016 14:47:44 GMT"
},
{
"version": "v3",
"created": "Fri, 23 Sep 2016 15:16:46 GMT"
}
] | 2016-09-26T00:00:00 | [
[
"Lebret",
"Remi",
""
],
[
"Grangier",
"David",
""
],
[
"Auli",
"Michael",
""
]
] | TITLE: Neural Text Generation from Structured Data with Application to the
Biography Domain
ABSTRACT: This paper introduces a neural model for concept-to-text generation that
scales to large, rich domains. We experiment with a new dataset of biographies
from Wikipedia that is an order of magnitude larger than existing resources
with over 700k samples. The dataset is also vastly more diverse with a 400k
vocabulary, compared to a few hundred words for Weathergov or Robocup. Our
model builds upon recent work on conditional neural language model for text
generation. To deal with the large vocabulary, we extend these models to mix a
fixed vocabulary with copy actions that transfer sample-specific words from the
input database to the generated output sentence. Our neural model significantly
out-performs a classical Kneser-Ney language model adapted to this task by
nearly 15 BLEU.
| new_dataset | 0.952794 |
1608.04117 | Michal Drozdzal | Michal Drozdzal, Eugene Vorontsov, Gabriel Chartrand, Samuel Kadoury,
Chris Pal | The Importance of Skip Connections in Biomedical Image Segmentation | Accepted to 2nd Workshop on Deep Learning in Medical Image Analysis
(DLMIA 2016); Added references | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study the influence of both long and short skip connections
on Fully Convolutional Networks (FCN) for biomedical image segmentation. In
standard FCNs, only long skip connections are used to skip features from the
contracting path to the expanding path in order to recover spatial information
lost during downsampling. We extend FCNs by adding short skip connections, that
are similar to the ones introduced in residual networks, in order to build very
deep FCNs (of hundreds of layers). A review of the gradient flow confirms that
for a very deep FCN it is beneficial to have both long and short skip
connections. Finally, we show that a very deep FCN can achieve
near-to-state-of-the-art results on the EM dataset without any further
post-processing.
| [
{
"version": "v1",
"created": "Sun, 14 Aug 2016 17:10:30 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Sep 2016 20:14:09 GMT"
}
] | 2016-09-26T00:00:00 | [
[
"Drozdzal",
"Michal",
""
],
[
"Vorontsov",
"Eugene",
""
],
[
"Chartrand",
"Gabriel",
""
],
[
"Kadoury",
"Samuel",
""
],
[
"Pal",
"Chris",
""
]
] | TITLE: The Importance of Skip Connections in Biomedical Image Segmentation
ABSTRACT: In this paper, we study the influence of both long and short skip connections
on Fully Convolutional Networks (FCN) for biomedical image segmentation. In
standard FCNs, only long skip connections are used to skip features from the
contracting path to the expanding path in order to recover spatial information
lost during downsampling. We extend FCNs by adding short skip connections, that
are similar to the ones introduced in residual networks, in order to build very
deep FCNs (of hundreds of layers). A review of the gradient flow confirms that
for a very deep FCN it is beneficial to have both long and short skip
connections. Finally, we show that a very deep FCN can achieve
near-to-state-of-the-art results on the EM dataset without any further
post-processing.
| no_new_dataset | 0.957755 |
1609.05158 | Wenzhe Shi | Wenzhe Shi, Jose Caballero, Ferenc Husz\'ar, Johannes Totz, Andrew P.
Aitken, Rob Bishop, Daniel Rueckert and Zehan Wang | Real-Time Single Image and Video Super-Resolution Using an Efficient
Sub-Pixel Convolutional Neural Network | CVPR 2016 paper with updated affiliations and supplemental material,
fixed typo in equation 4 | null | null | null | cs.CV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, several models based on deep neural networks have achieved great
success in terms of both reconstruction accuracy and computational performance
for single image super-resolution. In these methods, the low resolution (LR)
input image is upscaled to the high resolution (HR) space using a single
filter, commonly bicubic interpolation, before reconstruction. This means that
the super-resolution (SR) operation is performed in HR space. We demonstrate
that this is sub-optimal and adds computational complexity. In this paper, we
present the first convolutional neural network (CNN) capable of real-time SR of
1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN
architecture where the feature maps are extracted in the LR space. In addition,
we introduce an efficient sub-pixel convolution layer which learns an array of
upscaling filters to upscale the final LR feature maps into the HR output. By
doing so, we effectively replace the handcrafted bicubic filter in the SR
pipeline with more complex upscaling filters specifically trained for each
feature map, whilst also reducing the computational complexity of the overall
SR operation. We evaluate the proposed approach using images and videos from
publicly available datasets and show that it performs significantly better
(+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster
than previous CNN-based methods.
| [
{
"version": "v1",
"created": "Fri, 16 Sep 2016 17:58:14 GMT"
},
{
"version": "v2",
"created": "Fri, 23 Sep 2016 17:16:37 GMT"
}
] | 2016-09-26T00:00:00 | [
[
"Shi",
"Wenzhe",
""
],
[
"Caballero",
"Jose",
""
],
[
"Huszár",
"Ferenc",
""
],
[
"Totz",
"Johannes",
""
],
[
"Aitken",
"Andrew P.",
""
],
[
"Bishop",
"Rob",
""
],
[
"Rueckert",
"Daniel",
""
],
[
"Wang",
"Zehan",
""
]
] | TITLE: Real-Time Single Image and Video Super-Resolution Using an Efficient
Sub-Pixel Convolutional Neural Network
ABSTRACT: Recently, several models based on deep neural networks have achieved great
success in terms of both reconstruction accuracy and computational performance
for single image super-resolution. In these methods, the low resolution (LR)
input image is upscaled to the high resolution (HR) space using a single
filter, commonly bicubic interpolation, before reconstruction. This means that
the super-resolution (SR) operation is performed in HR space. We demonstrate
that this is sub-optimal and adds computational complexity. In this paper, we
present the first convolutional neural network (CNN) capable of real-time SR of
1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN
architecture where the feature maps are extracted in the LR space. In addition,
we introduce an efficient sub-pixel convolution layer which learns an array of
upscaling filters to upscale the final LR feature maps into the HR output. By
doing so, we effectively replace the handcrafted bicubic filter in the SR
pipeline with more complex upscaling filters specifically trained for each
feature map, whilst also reducing the computational complexity of the overall
SR operation. We evaluate the proposed approach using images and videos from
publicly available datasets and show that it performs significantly better
(+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster
than previous CNN-based methods.
| no_new_dataset | 0.952486 |
1609.06423 | Mayank Singh | Mayank Singh, Barnopriyo Barua, Priyank Palod, Manvi Garg, Sidhartha
Satapathy, Samuel Bushi, Kumar Ayush, Krishna Sai Rohith, Tulasi Gamidi,
Pawan Goyal and Animesh Mukherjee | OCR++: A Robust Framework For Information Extraction from Scholarly
Articles | null | null | null | null | cs.DL cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes OCR++, an open-source framework designed for a variety of
information extraction tasks from scholarly articles including metadata (title,
author names, affiliation and e-mail), structure (section headings and body
text, table and figure headings, URLs and footnotes) and bibliography (citation
instances and references). We analyze a diverse set of scientific articles
written in English language to understand generic writing patterns and
formulate rules to develop this hybrid framework. Extensive evaluations show
that the proposed framework outperforms the existing state-of-the-art tools
with huge margin in structural information extraction along with improved
performance in metadata and bibliography extraction tasks, both in terms of
accuracy (around 50% improvement) and processing time (around 52% improvement).
A user experience study conducted with the help of 30 researchers reveals that
the researchers found this system to be very helpful. As an additional
objective, we discuss two novel use cases including automatically extracting
links to public datasets from the proceedings, which would further accelerate
the advancement in digital libraries. The result of the framework can be
exported as a whole into structured TEI-encoded documents. Our framework is
accessible online at http://cnergres.iitkgp.ac.in/OCR++/home/.
| [
{
"version": "v1",
"created": "Wed, 21 Sep 2016 06:12:52 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Sep 2016 10:54:57 GMT"
},
{
"version": "v3",
"created": "Fri, 23 Sep 2016 13:05:27 GMT"
}
] | 2016-09-26T00:00:00 | [
[
"Singh",
"Mayank",
""
],
[
"Barua",
"Barnopriyo",
""
],
[
"Palod",
"Priyank",
""
],
[
"Garg",
"Manvi",
""
],
[
"Satapathy",
"Sidhartha",
""
],
[
"Bushi",
"Samuel",
""
],
[
"Ayush",
"Kumar",
""
],
[
"Rohith",
"Krishna Sai",
""
],
[
"Gamidi",
"Tulasi",
""
],
[
"Goyal",
"Pawan",
""
],
[
"Mukherjee",
"Animesh",
""
]
] | TITLE: OCR++: A Robust Framework For Information Extraction from Scholarly
Articles
ABSTRACT: This paper proposes OCR++, an open-source framework designed for a variety of
information extraction tasks from scholarly articles including metadata (title,
author names, affiliation and e-mail), structure (section headings and body
text, table and figure headings, URLs and footnotes) and bibliography (citation
instances and references). We analyze a diverse set of scientific articles
written in English language to understand generic writing patterns and
formulate rules to develop this hybrid framework. Extensive evaluations show
that the proposed framework outperforms the existing state-of-the-art tools
with huge margin in structural information extraction along with improved
performance in metadata and bibliography extraction tasks, both in terms of
accuracy (around 50% improvement) and processing time (around 52% improvement).
A user experience study conducted with the help of 30 researchers reveals that
the researchers found this system to be very helpful. As an additional
objective, we discuss two novel use cases including automatically extracting
links to public datasets from the proceedings, which would further accelerate
the advancement in digital libraries. The result of the framework can be
exported as a whole into structured TEI-encoded documents. Our framework is
accessible online at http://cnergres.iitkgp.ac.in/OCR++/home/.
| no_new_dataset | 0.948346 |
1609.07170 | Alexander Wong | Prajna Paramita Dash, Akshaya Mishra, and Alexander Wong | Deep Quality: A Deep No-reference Quality Assessment System | 2 pages | null | null | null | cs.MM cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Image quality assessment (IQA) continues to garner great interest in the
research community, particularly given the tremendous rise in consumer video
capture and streaming. Despite significant research effort in IQA in the past
few decades, the area of no-reference image quality assessment remains a great
challenge and is largely unsolved. In this paper, we propose a novel
no-reference image quality assessment system called Deep Quality, which
leverages the power of deep learning to model the complex relationship between
visual content and the perceived quality. Deep Quality consists of a novel
multi-scale deep convolutional neural network, trained to learn to assess image
quality based on training samples consisting of different distortions and
degradations such as blur, Gaussian noise, and compression artifacts.
Preliminary results using the CSIQ benchmark image quality dataset showed that
Deep Quality was able to achieve strong quality prediction performance (89%
patch-level and 98% image-level prediction accuracy), being able to achieve
similar performance as full-reference IQA methods.
| [
{
"version": "v1",
"created": "Thu, 22 Sep 2016 21:26:21 GMT"
}
] | 2016-09-26T00:00:00 | [
[
"Dash",
"Prajna Paramita",
""
],
[
"Mishra",
"Akshaya",
""
],
[
"Wong",
"Alexander",
""
]
] | TITLE: Deep Quality: A Deep No-reference Quality Assessment System
ABSTRACT: Image quality assessment (IQA) continues to garner great interest in the
research community, particularly given the tremendous rise in consumer video
capture and streaming. Despite significant research effort in IQA in the past
few decades, the area of no-reference image quality assessment remains a great
challenge and is largely unsolved. In this paper, we propose a novel
no-reference image quality assessment system called Deep Quality, which
leverages the power of deep learning to model the complex relationship between
visual content and the perceived quality. Deep Quality consists of a novel
multi-scale deep convolutional neural network, trained to learn to assess image
quality based on training samples consisting of different distortions and
degradations such as blur, Gaussian noise, and compression artifacts.
Preliminary results using the CSIQ benchmark image quality dataset showed that
Deep Quality was able to achieve strong quality prediction performance (89%
patch-level and 98% image-level prediction accuracy), being able to achieve
similar performance as full-reference IQA methods.
| no_new_dataset | 0.947088 |
1609.07215 | Rajasekar Venkatesan | Mihika Dave, Sahil Tapiawala, Meng Joo Er, Rajasekar Venkatesan | A Novel Progressive Multi-label Classifier for Classincremental Data | 5 pages, 3 figures, 4 tables | null | null | null | cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, a progressive learning algorithm for multi-label
classification to learn new labels while retaining the knowledge of previous
labels is designed. New output neurons corresponding to new labels are added
and the neural network connections and parameters are automatically
restructured as if the label has been introduced from the beginning. This work
is the first of the kind in multi-label classifier for class-incremental
learning. It is useful for real-world applications such as robotics where
streaming data are available and the number of labels is often unknown. Based
on the Extreme Learning Machine framework, a novel universal classifier with
plug and play capabilities for progressive multi-label classification is
developed. Experimental results on various benchmark synthetic and real
datasets validate the efficiency and effectiveness of our proposed algorithm.
| [
{
"version": "v1",
"created": "Fri, 23 Sep 2016 03:09:24 GMT"
}
] | 2016-09-26T00:00:00 | [
[
"Dave",
"Mihika",
""
],
[
"Tapiawala",
"Sahil",
""
],
[
"Er",
"Meng Joo",
""
],
[
"Venkatesan",
"Rajasekar",
""
]
] | TITLE: A Novel Progressive Multi-label Classifier for Classincremental Data
ABSTRACT: In this paper, a progressive learning algorithm for multi-label
classification to learn new labels while retaining the knowledge of previous
labels is designed. New output neurons corresponding to new labels are added
and the neural network connections and parameters are automatically
restructured as if the label has been introduced from the beginning. This work
is the first of the kind in multi-label classifier for class-incremental
learning. It is useful for real-world applications such as robotics where
streaming data are available and the number of labels is often unknown. Based
on the Extreme Learning Machine framework, a novel universal classifier with
plug and play capabilities for progressive multi-label classification is
developed. Experimental results on various benchmark synthetic and real
datasets validate the efficiency and effectiveness of our proposed algorithm.
| no_new_dataset | 0.953751 |
1609.07306 | Helge Rhodin | Helge Rhodin, Christian Richardt, Dan Casas, Eldar Insafutdinov,
Mohammad Shafiei, Hans-Peter Seidel, Bernt Schiele, Christian Theobalt | EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras | SIGGRAPH Asia 2016 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Marker-based and marker-less optical skeletal motion-capture methods use an
outside-in arrangement of cameras placed around a scene, with viewpoints
converging on the center. They often create discomfort by possibly needed
marker suits, and their recording volume is severely restricted and often
constrained to indoor scenes with controlled backgrounds. Alternative
suit-based systems use several inertial measurement units or an exoskeleton to
capture motion. This makes capturing independent of a confined volume, but
requires substantial, often constraining, and hard to set up body
instrumentation. We therefore propose a new method for real-time, marker-less
and egocentric motion capture which estimates the full-body skeleton pose from
a lightweight stereo pair of fisheye cameras that are attached to a helmet or
virtual reality headset. It combines the strength of a new generative pose
estimation framework for fisheye views with a ConvNet-based body-part detector
trained on a large new dataset. Our inside-in method captures full-body motion
in general indoor and outdoor scenes, and also crowded scenes with many people
in close vicinity. The captured user can freely move around, which enables
reconstruction of larger-scale activities and is particularly useful in virtual
reality to freely roam and interact, while seeing the fully motion-captured
virtual body.
| [
{
"version": "v1",
"created": "Fri, 23 Sep 2016 10:46:19 GMT"
}
] | 2016-09-26T00:00:00 | [
[
"Rhodin",
"Helge",
""
],
[
"Richardt",
"Christian",
""
],
[
"Casas",
"Dan",
""
],
[
"Insafutdinov",
"Eldar",
""
],
[
"Shafiei",
"Mohammad",
""
],
[
"Seidel",
"Hans-Peter",
""
],
[
"Schiele",
"Bernt",
""
],
[
"Theobalt",
"Christian",
""
]
] | TITLE: EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras
ABSTRACT: Marker-based and marker-less optical skeletal motion-capture methods use an
outside-in arrangement of cameras placed around a scene, with viewpoints
converging on the center. They often create discomfort by possibly needed
marker suits, and their recording volume is severely restricted and often
constrained to indoor scenes with controlled backgrounds. Alternative
suit-based systems use several inertial measurement units or an exoskeleton to
capture motion. This makes capturing independent of a confined volume, but
requires substantial, often constraining, and hard to set up body
instrumentation. We therefore propose a new method for real-time, marker-less
and egocentric motion capture which estimates the full-body skeleton pose from
a lightweight stereo pair of fisheye cameras that are attached to a helmet or
virtual reality headset. It combines the strength of a new generative pose
estimation framework for fisheye views with a ConvNet-based body-part detector
trained on a large new dataset. Our inside-in method captures full-body motion
in general indoor and outdoor scenes, and also crowded scenes with many people
in close vicinity. The captured user can freely move around, which enables
reconstruction of larger-scale activities and is particularly useful in virtual
reality to freely roam and interact, while seeing the fully motion-captured
virtual body.
| new_dataset | 0.956836 |
1609.07349 | Vincent Primault | Vincent Primault (INSA Lyon, DRIM), Antoine Boutet (DRIM, INSA Lyon),
Sonia Ben Mokhtar (DRIM, INSA Lyon), Lionel Brunie (DRIM, INSA Lyon) | Adaptive Location Privacy with ALP | 35th Symposium on Reliable Distributed Systems, Sep 2016, Budapest,
Hungary | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the increasing amount of mobility data being collected on a daily basis
by location-based services (LBSs) comes a new range of threats for users,
related to the over-sharing of their location information. To deal with this
issue, several location privacy protection mechanisms (LPPMs) have been
proposed in the past years. However, each of these mechanisms comes with
different configuration parameters that have a direct impact both on the
privacy guarantees offered to the users and on the resulting utility of the
protected data. In this context, it can be difficult for non-expert system
designers to choose the appropriate configuration to use. Moreover, these
mechanisms are generally configured once for all, which results in the same
configuration for every protected piece of information. However, not all users
have the same behaviour, and even the behaviour of a single user is likely to
change over time. To address this issue, we present in this paper ALP, a new
framework enabling the dynamic configuration of LPPMs. ALP can be used in two
scenarios: (1) offline, where ALP enables a system designer to choose and
automatically tune the most appropriate LPPM for the protection of a given
dataset; (2) online, where ALP enables the user of a crowd sensing application
to protect consecutive batches of her geolocated data by automatically tuning
an existing LPPM to fulfil a set of privacy and utility objectives. We evaluate
ALP on both scenarios with two real-life mobility datasets and two
state-of-the-art LPPMs. Our experiments show that the adaptive LPPM
configurations found by ALP outperform both in terms of privacy and utility a
set of static configurations manually fixed by a system designer.
| [
{
"version": "v1",
"created": "Fri, 23 Sep 2016 13:19:18 GMT"
}
] | 2016-09-26T00:00:00 | [
[
"Primault",
"Vincent",
"",
"INSA Lyon, DRIM"
],
[
"Boutet",
"Antoine",
"",
"DRIM, INSA Lyon"
],
[
"Mokhtar",
"Sonia Ben",
"",
"DRIM, INSA Lyon"
],
[
"Brunie",
"Lionel",
"",
"DRIM, INSA Lyon"
]
] | TITLE: Adaptive Location Privacy with ALP
ABSTRACT: With the increasing amount of mobility data being collected on a daily basis
by location-based services (LBSs) comes a new range of threats for users,
related to the over-sharing of their location information. To deal with this
issue, several location privacy protection mechanisms (LPPMs) have been
proposed in the past years. However, each of these mechanisms comes with
different configuration parameters that have a direct impact both on the
privacy guarantees offered to the users and on the resulting utility of the
protected data. In this context, it can be difficult for non-expert system
designers to choose the appropriate configuration to use. Moreover, these
mechanisms are generally configured once for all, which results in the same
configuration for every protected piece of information. However, not all users
have the same behaviour, and even the behaviour of a single user is likely to
change over time. To address this issue, we present in this paper ALP, a new
framework enabling the dynamic configuration of LPPMs. ALP can be used in two
scenarios: (1) offline, where ALP enables a system designer to choose and
automatically tune the most appropriate LPPM for the protection of a given
dataset; (2) online, where ALP enables the user of a crowd sensing application
to protect consecutive batches of her geolocated data by automatically tuning
an existing LPPM to fulfil a set of privacy and utility objectives. We evaluate
ALP on both scenarios with two real-life mobility datasets and two
state-of-the-art LPPMs. Our experiments show that the adaptive LPPM
configurations found by ALP outperform both in terms of privacy and utility a
set of static configurations manually fixed by a system designer.
| no_new_dataset | 0.948917 |
1609.07420 | Marko Linna | Marko Linna, Juho Kannala, Esa Rahtu | Real-time Human Pose Estimation from Video with Convolutional Neural
Networks | 16 pages | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present a method for real-time multi-person human pose
estimation from video by utilizing convolutional neural networks. Our method is
aimed for use case specific applications, where good accuracy is essential and
variation of the background and poses is limited. This enables us to use a
generic network architecture, which is both accurate and fast. We divide the
problem into two phases: (1) pre-training and (2) finetuning. In pre-training,
the network is learned with highly diverse input data from publicly available
datasets, while in finetuning we train with application specific data, which we
record with Kinect. Our method differs from most of the state-of-the-art
methods in that we consider the whole system, including person detector, pose
estimator and an automatic way to record application specific training material
for finetuning. Our method is considerably faster than many of the
state-of-the-art methods. Our method can be thought of as a replacement for
Kinect, and it can be used for higher level tasks, such as gesture control,
games, person tracking, action recognition and action tracking. We achieved
accuracy of 96.8\% ([email protected]) with application specific data.
| [
{
"version": "v1",
"created": "Fri, 23 Sep 2016 16:22:59 GMT"
}
] | 2016-09-26T00:00:00 | [
[
"Linna",
"Marko",
""
],
[
"Kannala",
"Juho",
""
],
[
"Rahtu",
"Esa",
""
]
] | TITLE: Real-time Human Pose Estimation from Video with Convolutional Neural
Networks
ABSTRACT: In this paper, we present a method for real-time multi-person human pose
estimation from video by utilizing convolutional neural networks. Our method is
aimed for use case specific applications, where good accuracy is essential and
variation of the background and poses is limited. This enables us to use a
generic network architecture, which is both accurate and fast. We divide the
problem into two phases: (1) pre-training and (2) finetuning. In pre-training,
the network is learned with highly diverse input data from publicly available
datasets, while in finetuning we train with application specific data, which we
record with Kinect. Our method differs from most of the state-of-the-art
methods in that we consider the whole system, including person detector, pose
estimator and an automatic way to record application specific training material
for finetuning. Our method is considerably faster than many of the
state-of-the-art methods. Our method can be thought of as a replacement for
Kinect, and it can be used for higher level tasks, such as gesture control,
games, person tracking, action recognition and action tracking. We achieved
accuracy of 96.8\% ([email protected]) with application specific data.
| no_new_dataset | 0.948489 |
1609.07451 | Linfeng Song | Linfeng Song, Yue Zhang, Xiaochang Peng, Zhiguo Wang and Daniel Gildea | AMR-to-text generation as a Traveling Salesman Problem | accepted by EMNLP 2016 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The task of AMR-to-text generation is to generate grammatical text that
sustains the semantic meaning for a given AMR graph. We at- tack the task by
first partitioning the AMR graph into smaller fragments, and then generating
the translation for each fragment, before finally deciding the order by solving
an asymmetric generalized traveling salesman problem (AGTSP). A Maximum Entropy
classifier is trained to estimate the traveling costs, and a TSP solver is used
to find the optimized solution. The final model reports a BLEU score of 22.44
on the SemEval-2016 Task8 dataset.
| [
{
"version": "v1",
"created": "Fri, 23 Sep 2016 18:12:12 GMT"
}
] | 2016-09-26T00:00:00 | [
[
"Song",
"Linfeng",
""
],
[
"Zhang",
"Yue",
""
],
[
"Peng",
"Xiaochang",
""
],
[
"Wang",
"Zhiguo",
""
],
[
"Gildea",
"Daniel",
""
]
] | TITLE: AMR-to-text generation as a Traveling Salesman Problem
ABSTRACT: The task of AMR-to-text generation is to generate grammatical text that
sustains the semantic meaning for a given AMR graph. We at- tack the task by
first partitioning the AMR graph into smaller fragments, and then generating
the translation for each fragment, before finally deciding the order by solving
an asymmetric generalized traveling salesman problem (AGTSP). A Maximum Entropy
classifier is trained to estimate the traveling costs, and a TSP solver is used
to find the optimized solution. The final model reports a BLEU score of 22.44
on the SemEval-2016 Task8 dataset.
| no_new_dataset | 0.955068 |
1309.1114 | Marzia Rivi | Marzia Rivi, Claudio Gheller, Tim Dykes, Mel Krokos, Klaus Dolag | GPU Accelerated Particle Visualization with Splotch | 25 pages, 9 figures. Astronomy and Computing (2014) | Astronomy and Computing 2014, 5: 9-18 | 10.1016/j.ascom.2014.03.001 | null | astro-ph.IM cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Splotch is a rendering algorithm for exploration and visual discovery in
particle-based datasets coming from astronomical observations or numerical
simulations. The strengths of the approach are production of high quality
imagery and support for very large-scale datasets through an effective mix of
the OpenMP and MPI parallel programming paradigms. This article reports our
experiences in re-designing Splotch for exploiting emerging HPC architectures
nowadays increasingly populated with GPUs. A performance model is introduced
for data transfers, computations and memory access, to guide our re-factoring
of Splotch. A number of parallelization issues are discussed, in particular
relating to race conditions and workload balancing, towards achieving optimal
performances. Our implementation was accomplished by using the CUDA programming
paradigm. Our strategy is founded on novel schemes achieving optimized data
organisation and classification of particles. We deploy a reference simulation
to present performance results on acceleration gains and scalability. We
finally outline our vision for future work developments including possibilities
for further optimisations and exploitation of emerging technologies.
| [
{
"version": "v1",
"created": "Wed, 4 Sep 2013 17:36:46 GMT"
},
{
"version": "v2",
"created": "Sun, 23 Mar 2014 18:18:03 GMT"
}
] | 2016-09-23T00:00:00 | [
[
"Rivi",
"Marzia",
""
],
[
"Gheller",
"Claudio",
""
],
[
"Dykes",
"Tim",
""
],
[
"Krokos",
"Mel",
""
],
[
"Dolag",
"Klaus",
""
]
] | TITLE: GPU Accelerated Particle Visualization with Splotch
ABSTRACT: Splotch is a rendering algorithm for exploration and visual discovery in
particle-based datasets coming from astronomical observations or numerical
simulations. The strengths of the approach are production of high quality
imagery and support for very large-scale datasets through an effective mix of
the OpenMP and MPI parallel programming paradigms. This article reports our
experiences in re-designing Splotch for exploiting emerging HPC architectures
nowadays increasingly populated with GPUs. A performance model is introduced
for data transfers, computations and memory access, to guide our re-factoring
of Splotch. A number of parallelization issues are discussed, in particular
relating to race conditions and workload balancing, towards achieving optimal
performances. Our implementation was accomplished by using the CUDA programming
paradigm. Our strategy is founded on novel schemes achieving optimized data
organisation and classification of particles. We deploy a reference simulation
to present performance results on acceleration gains and scalability. We
finally outline our vision for future work developments including possibilities
for further optimisations and exploitation of emerging technologies.
| no_new_dataset | 0.942401 |
1510.05727 | Patrick Huck | Patrick Huck, Dan Gunter, Shreyas Cholia, Donald Winston, Alpha
N'Diaye, Kristin Persson | User Applications Driven by the Community Contribution Framework
MPContribs in the Materials Project | 12 pages, 5 figures, Proceedings of 10th Gateway Computing
Environments Workshop (2015), to be published in "Concurrency in Computation:
Practice and Experience" | Concurrency and Computation: Practice and Experience Vol. 28 Nr. 7
p.1982-1993 | 10.1002/cpe.3698 | null | cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work discusses how the MPContribs framework in the Materials Project
(MP) allows user-contributed data to be shown and analyzed alongside the core
MP database. The Materials Project is a searchable database of electronic
structure properties of over 65,000 bulk solid materials that is accessible
through a web-based science-gateway. We describe the motivation for enabling
user contributions to the materials data and present the framework's features
and challenges in the context of two real applications. These use-cases
illustrate how scientific collaborations can build applications with their own
"user-contributed" data using MPContribs. The Nanoporous Materials Explorer
application provides a unique search interface to a novel dataset of hundreds
of thousands of materials, each with tables of user-contributed values related
to material adsorption and density at varying temperature and pressure. The
Unified Theoretical and Experimental x-ray Spectroscopy application discusses a
full workflow for the association, dissemination and combined analyses of
experimental data from the Advanced Light Source with MP's theoretical core
data, using MPContribs tools for data formatting, management and exploration.
The capabilities being developed for these collaborations are serving as the
model for how new materials data can be incorporated into the Materials Project
website with minimal staff overhead while giving powerful tools for data search
and display to the user community.
| [
{
"version": "v1",
"created": "Tue, 20 Oct 2015 00:55:50 GMT"
}
] | 2016-09-23T00:00:00 | [
[
"Huck",
"Patrick",
""
],
[
"Gunter",
"Dan",
""
],
[
"Cholia",
"Shreyas",
""
],
[
"Winston",
"Donald",
""
],
[
"N'Diaye",
"Alpha",
""
],
[
"Persson",
"Kristin",
""
]
] | TITLE: User Applications Driven by the Community Contribution Framework
MPContribs in the Materials Project
ABSTRACT: This work discusses how the MPContribs framework in the Materials Project
(MP) allows user-contributed data to be shown and analyzed alongside the core
MP database. The Materials Project is a searchable database of electronic
structure properties of over 65,000 bulk solid materials that is accessible
through a web-based science-gateway. We describe the motivation for enabling
user contributions to the materials data and present the framework's features
and challenges in the context of two real applications. These use-cases
illustrate how scientific collaborations can build applications with their own
"user-contributed" data using MPContribs. The Nanoporous Materials Explorer
application provides a unique search interface to a novel dataset of hundreds
of thousands of materials, each with tables of user-contributed values related
to material adsorption and density at varying temperature and pressure. The
Unified Theoretical and Experimental x-ray Spectroscopy application discusses a
full workflow for the association, dissemination and combined analyses of
experimental data from the Advanced Light Source with MP's theoretical core
data, using MPContribs tools for data formatting, management and exploration.
The capabilities being developed for these collaborations are serving as the
model for how new materials data can be incorporated into the Materials Project
website with minimal staff overhead while giving powerful tools for data search
and display to the user community.
| new_dataset | 0.894789 |
1604.06318 | Nikolay Savinov | Dmitry Laptev, Nikolay Savinov, Joachim M. Buhmann, Marc Pollefeys | TI-POOLING: transformation-invariant pooling for feature learning in
Convolutional Neural Networks | Accepted at CVPR 2016. The first two authors assert equal
contribution and joint first authorship | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present a deep neural network topology that incorporates a
simple to implement transformation invariant pooling operator (TI-POOLING).
This operator is able to efficiently handle prior knowledge on nuisance
variations in the data, such as rotation or scale changes. Most current methods
usually make use of dataset augmentation to address this issue, but this
requires larger number of model parameters and more training data, and results
in significantly increased training time and larger chance of under- or
overfitting. The main reason for these drawbacks is that the learned model
needs to capture adequate features for all the possible transformations of the
input. On the other hand, we formulate features in convolutional neural
networks to be transformation-invariant. We achieve that using parallel siamese
architectures for the considered transformation set and applying the TI-POOLING
operator on their outputs before the fully-connected layers. We show that this
topology internally finds the most optimal "canonical" instance of the input
image for training and therefore limits the redundancy in learned features.
This more efficient use of training data results in better performance on
popular benchmark datasets with smaller number of parameters when comparing to
standard convolutional neural networks with dataset augmentation and to other
baselines.
| [
{
"version": "v1",
"created": "Thu, 21 Apr 2016 14:17:05 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Sep 2016 14:42:28 GMT"
}
] | 2016-09-23T00:00:00 | [
[
"Laptev",
"Dmitry",
""
],
[
"Savinov",
"Nikolay",
""
],
[
"Buhmann",
"Joachim M.",
""
],
[
"Pollefeys",
"Marc",
""
]
] | TITLE: TI-POOLING: transformation-invariant pooling for feature learning in
Convolutional Neural Networks
ABSTRACT: In this paper we present a deep neural network topology that incorporates a
simple to implement transformation invariant pooling operator (TI-POOLING).
This operator is able to efficiently handle prior knowledge on nuisance
variations in the data, such as rotation or scale changes. Most current methods
usually make use of dataset augmentation to address this issue, but this
requires larger number of model parameters and more training data, and results
in significantly increased training time and larger chance of under- or
overfitting. The main reason for these drawbacks is that the learned model
needs to capture adequate features for all the possible transformations of the
input. On the other hand, we formulate features in convolutional neural
networks to be transformation-invariant. We achieve that using parallel siamese
architectures for the considered transformation set and applying the TI-POOLING
operator on their outputs before the fully-connected layers. We show that this
topology internally finds the most optimal "canonical" instance of the input
image for training and therefore limits the redundancy in learned features.
This more efficient use of training data results in better performance on
popular benchmark datasets with smaller number of parameters when comparing to
standard convolutional neural networks with dataset augmentation and to other
baselines.
| no_new_dataset | 0.949153 |
1604.06629 | Giulio Cimini | Giulio Cimini and Matteo Serri | Entangling credit and funding shocks in interbank markets | null | PLoS ONE 11(8): e0161642 (2016) | 10.1371/journal.pone.0161642 | null | q-fin.RM physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Credit and liquidity risks represent main channels of financial contagion for
interbank lending markets. On one hand, banks face potential losses whenever
their counterparties are under distress and thus unable to fulfill their
obligations. On the other hand, solvency constraints may force banks to recover
lost fundings by selling their illiquid assets, resulting in effective losses
in the presence of fire sales - that is, when funding shortcomings are
widespread over the market. Because of the complex structure of the network of
interbank exposures, these losses reverberate among banks and eventually get
amplified, with potentially catastrophic consequences for the whole financial
system. Building on Debt Rank [Battiston et al., 2012], in this work we define
a systemic risk metric that estimates the potential amplification of losses in
interbank markets accounting for both credit and liquidity contagion channels:
the Debt-Solvency Rank. We implement this framework on a dataset of 183
European banks that were publicly traded between 2004 and 2013, showing indeed
that liquidity spillovers substantially increase systemic risk, and thus cannot
be neglected in stress-test scenarios. We also provide additional evidence that
the interbank market was extremely fragile up to the 2008 financial crisis,
becoming slightly more robust only afterwards.
| [
{
"version": "v1",
"created": "Fri, 22 Apr 2016 12:39:36 GMT"
}
] | 2016-09-23T00:00:00 | [
[
"Cimini",
"Giulio",
""
],
[
"Serri",
"Matteo",
""
]
] | TITLE: Entangling credit and funding shocks in interbank markets
ABSTRACT: Credit and liquidity risks represent main channels of financial contagion for
interbank lending markets. On one hand, banks face potential losses whenever
their counterparties are under distress and thus unable to fulfill their
obligations. On the other hand, solvency constraints may force banks to recover
lost fundings by selling their illiquid assets, resulting in effective losses
in the presence of fire sales - that is, when funding shortcomings are
widespread over the market. Because of the complex structure of the network of
interbank exposures, these losses reverberate among banks and eventually get
amplified, with potentially catastrophic consequences for the whole financial
system. Building on Debt Rank [Battiston et al., 2012], in this work we define
a systemic risk metric that estimates the potential amplification of losses in
interbank markets accounting for both credit and liquidity contagion channels:
the Debt-Solvency Rank. We implement this framework on a dataset of 183
European banks that were publicly traded between 2004 and 2013, showing indeed
that liquidity spillovers substantially increase systemic risk, and thus cannot
be neglected in stress-test scenarios. We also provide additional evidence that
the interbank market was extremely fragile up to the 2008 financial crisis,
becoming slightly more robust only afterwards.
| no_new_dataset | 0.92157 |
1609.06845 | Sebastien Lefevre | Nicolas Audebert (OBELIX, Palaiseau), Bertrand Le Saux (Palaiseau),
S\'ebastien Lef\`evre (OBELIX) | On the usability of deep networks for object-based image analysis | in International Conference on Geographic Object-Based Image Analysis
(GEOBIA), Sep 2016, Enschede, Netherlands | null | null | null | cs.NE cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As computer vision before, remote sensing has been radically changed by the
introduction of Convolution Neural Networks. Land cover use, object detection
and scene understanding in aerial images rely more and more on deep learning to
achieve new state-of-the-art results. Recent architectures such as Fully
Convolutional Networks (Long et al., 2015) can even produce pixel level
annotations for semantic mapping. In this work, we show how to use such deep
networks to detect, segment and classify different varieties of wheeled
vehicles in aerial images from the ISPRS Potsdam dataset. This allows us to
tackle object detection and classification on a complex dataset made up of
visually similar classes, and to demonstrate the relevance of such a subclass
modeling approach. Especially, we want to show that deep learning is also
suitable for object-oriented analysis of Earth Observation data. First, we
train a FCN variant on the ISPRS Potsdam dataset and show how the learnt
semantic maps can be used to extract precise segmentation of vehicles, which
allow us studying the repartition of vehicles in the city. Second, we train a
CNN to perform vehicle classification on the VEDAI (Razakarivony and Jurie,
2016) dataset, and transfer its knowledge to classify candidate segmented
vehicles on the Potsdam dataset.
| [
{
"version": "v1",
"created": "Thu, 22 Sep 2016 07:39:37 GMT"
}
] | 2016-09-23T00:00:00 | [
[
"Audebert",
"Nicolas",
"",
"OBELIX, Palaiseau"
],
[
"Saux",
"Bertrand Le",
"",
"Palaiseau"
],
[
"Lefèvre",
"Sébastien",
"",
"OBELIX"
]
] | TITLE: On the usability of deep networks for object-based image analysis
ABSTRACT: As computer vision before, remote sensing has been radically changed by the
introduction of Convolution Neural Networks. Land cover use, object detection
and scene understanding in aerial images rely more and more on deep learning to
achieve new state-of-the-art results. Recent architectures such as Fully
Convolutional Networks (Long et al., 2015) can even produce pixel level
annotations for semantic mapping. In this work, we show how to use such deep
networks to detect, segment and classify different varieties of wheeled
vehicles in aerial images from the ISPRS Potsdam dataset. This allows us to
tackle object detection and classification on a complex dataset made up of
visually similar classes, and to demonstrate the relevance of such a subclass
modeling approach. Especially, we want to show that deep learning is also
suitable for object-oriented analysis of Earth Observation data. First, we
train a FCN variant on the ISPRS Potsdam dataset and show how the learnt
semantic maps can be used to extract precise segmentation of vehicles, which
allow us studying the repartition of vehicles in the city. Second, we train a
CNN to perform vehicle classification on the VEDAI (Razakarivony and Jurie,
2016) dataset, and transfer its knowledge to classify candidate segmented
vehicles on the Potsdam dataset.
| no_new_dataset | 0.943243 |
1609.06846 | Nicolas Audebert | Nicolas Audebert (OBELIX, Palaiseau), Bertrand Le Saux (Palaiseau),
S\'ebastien Lef\`evre (OBELIX) | Semantic Segmentation of Earth Observation Data Using Multimodal and
Multi-scale Deep Networks | Asian Conference on Computer Vision (ACCV16), Nov 2016, Taipei,
Taiwan | null | null | null | cs.CV cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work investigates the use of deep fully convolutional neural networks
(DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially,
we train a variant of the SegNet architecture on remote sensing data over an
urban area and study different strategies for performing accurate semantic
segmentation. Our contributions are the following: 1) we transfer efficiently a
DFCNN from generic everyday images to remote sensing images; 2) we introduce a
multi-kernel convolutional layer for fast aggregation of predictions at
multiple scales; 3) we perform data fusion from heterogeneous sensors (optical
and laser) using residual correction. Our framework improves state-of-the-art
accuracy on the ISPRS Vaihingen 2D Semantic Labeling dataset.
| [
{
"version": "v1",
"created": "Thu, 22 Sep 2016 07:42:06 GMT"
}
] | 2016-09-23T00:00:00 | [
[
"Audebert",
"Nicolas",
"",
"OBELIX, Palaiseau"
],
[
"Saux",
"Bertrand Le",
"",
"Palaiseau"
],
[
"Lefèvre",
"Sébastien",
"",
"OBELIX"
]
] | TITLE: Semantic Segmentation of Earth Observation Data Using Multimodal and
Multi-scale Deep Networks
ABSTRACT: This work investigates the use of deep fully convolutional neural networks
(DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially,
we train a variant of the SegNet architecture on remote sensing data over an
urban area and study different strategies for performing accurate semantic
segmentation. Our contributions are the following: 1) we transfer efficiently a
DFCNN from generic everyday images to remote sensing images; 2) we introduce a
multi-kernel convolutional layer for fast aggregation of predictions at
multiple scales; 3) we perform data fusion from heterogeneous sensors (optical
and laser) using residual correction. Our framework improves state-of-the-art
accuracy on the ISPRS Vaihingen 2D Semantic Labeling dataset.
| no_new_dataset | 0.950041 |
1609.06896 | Dominik Alexander Klein | Dominik Alexander Klein, Dirk Schulz, Armin Bernd Cremers | Realtime Hierarchical Clustering based on Boundary and Surface
Statistics | Asian Conf. on Computer Vision (ACCV) 2016 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visual grouping is a key mechanism in human scene perception. There, it
belongs to the subconscious, early processing and is key prerequisite for other
high level tasks such as recognition. In this paper, we introduce an efficient,
realtime capable algorithm which likewise agglomerates a valuable hierarchical
clustering of a scene, while using purely local appearance statistics. To speed
up the processing, first we subdivide the image into meaningful, atomic
segments using a fast Watershed transform. Starting from there, our rapid,
agglomerative clustering algorithm prunes and maintains the connectivity graph
between clusters to contain only such pairs, which directly touch in the image
domain and are reciprocal nearest neighbors (RNN) wrt. a distance metric. The
core of this approach is our novel cluster distance: it combines boundary and
surface statistics both in terms of appearance as well as spatial linkage. This
yields state-of-the-art performance, as we demonstrate in conclusive
experiments conducted on BSDS500 and Pascal-Context datasets.
| [
{
"version": "v1",
"created": "Thu, 22 Sep 2016 10:17:30 GMT"
}
] | 2016-09-23T00:00:00 | [
[
"Klein",
"Dominik Alexander",
""
],
[
"Schulz",
"Dirk",
""
],
[
"Cremers",
"Armin Bernd",
""
]
] | TITLE: Realtime Hierarchical Clustering based on Boundary and Surface
Statistics
ABSTRACT: Visual grouping is a key mechanism in human scene perception. There, it
belongs to the subconscious, early processing and is key prerequisite for other
high level tasks such as recognition. In this paper, we introduce an efficient,
realtime capable algorithm which likewise agglomerates a valuable hierarchical
clustering of a scene, while using purely local appearance statistics. To speed
up the processing, first we subdivide the image into meaningful, atomic
segments using a fast Watershed transform. Starting from there, our rapid,
agglomerative clustering algorithm prunes and maintains the connectivity graph
between clusters to contain only such pairs, which directly touch in the image
domain and are reciprocal nearest neighbors (RNN) wrt. a distance metric. The
core of this approach is our novel cluster distance: it combines boundary and
surface statistics both in terms of appearance as well as spatial linkage. This
yields state-of-the-art performance, as we demonstrate in conclusive
experiments conducted on BSDS500 and Pascal-Context datasets.
| no_new_dataset | 0.951188 |
1609.06988 | Yuan Gao | Yuan Gao, Alan Yuille | Symmetric Non-Rigid Structure from Motion for Category-Specific Object
Structure Estimation | Accepted to ECCV 2016 | null | null | null | cs.CV cs.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many objects, especially these made by humans, are symmetric, e.g. cars and
aeroplanes. This paper addresses the estimation of 3D structures of symmetric
objects from multiple images of the same object category, e.g. different cars,
seen from various viewpoints. We assume that the deformation between different
instances from the same object category is non-rigid and symmetric. In this
paper, we extend two leading non-rigid structure from motion (SfM) algorithms
to exploit symmetry constraints. We model the both methods as energy
minimization, in which we also recover the missing observations caused by
occlusions. In particularly, we show that by rotating the coordinate system,
the energy can be decoupled into two independent terms, which still exploit
symmetry, to apply matrix factorization separately on each of them for
initialization. The results on the Pascal3D+ dataset show that our methods
significantly improve performance over baseline methods.
| [
{
"version": "v1",
"created": "Thu, 22 Sep 2016 13:57:10 GMT"
}
] | 2016-09-23T00:00:00 | [
[
"Gao",
"Yuan",
""
],
[
"Yuille",
"Alan",
""
]
] | TITLE: Symmetric Non-Rigid Structure from Motion for Category-Specific Object
Structure Estimation
ABSTRACT: Many objects, especially these made by humans, are symmetric, e.g. cars and
aeroplanes. This paper addresses the estimation of 3D structures of symmetric
objects from multiple images of the same object category, e.g. different cars,
seen from various viewpoints. We assume that the deformation between different
instances from the same object category is non-rigid and symmetric. In this
paper, we extend two leading non-rigid structure from motion (SfM) algorithms
to exploit symmetry constraints. We model the both methods as energy
minimization, in which we also recover the missing observations caused by
occlusions. In particularly, we show that by rotating the coordinate system,
the energy can be decoupled into two independent terms, which still exploit
symmetry, to apply matrix factorization separately on each of them for
initialization. The results on the Pascal3D+ dataset show that our methods
significantly improve performance over baseline methods.
| no_new_dataset | 0.94868 |
1609.07034 | Siddhartha Banerjee Siddhartha Banerjee | Siddhartha Banerjee, Prasenjit Mitra and Kazunari Sugiyama | Multi-document abstractive summarization using ILP based multi-sentence
compression | IJCAI'15 Proceedings of the 24th International Conference on
Artificial Intelligence, Pages 1208-1214, AAAI Press | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Abstractive summarization is an ideal form of summarization since it can
synthesize information from multiple documents to create concise informative
summaries. In this work, we aim at developing an abstractive summarizer. First,
our proposed approach identifies the most important document in the
multi-document set. The sentences in the most important document are aligned to
sentences in other documents to generate clusters of similar sentences. Second,
we generate K-shortest paths from the sentences in each cluster using a
word-graph structure. Finally, we select sentences from the set of shortest
paths generated from all the clusters employing a novel integer linear
programming (ILP) model with the objective of maximizing information content
and readability of the final summary. Our ILP model represents the shortest
paths as binary variables and considers the length of the path, information
score and linguistic quality score in the objective function. Experimental
results on the DUC 2004 and 2005 multi-document summarization datasets show
that our proposed approach outperforms all the baselines and state-of-the-art
extractive summarizers as measured by the ROUGE scores. Our method also
outperforms a recent abstractive summarization technique. In manual evaluation,
our approach also achieves promising results on informativeness and
readability.
| [
{
"version": "v1",
"created": "Thu, 22 Sep 2016 15:51:43 GMT"
}
] | 2016-09-23T00:00:00 | [
[
"Banerjee",
"Siddhartha",
""
],
[
"Mitra",
"Prasenjit",
""
],
[
"Sugiyama",
"Kazunari",
""
]
] | TITLE: Multi-document abstractive summarization using ILP based multi-sentence
compression
ABSTRACT: Abstractive summarization is an ideal form of summarization since it can
synthesize information from multiple documents to create concise informative
summaries. In this work, we aim at developing an abstractive summarizer. First,
our proposed approach identifies the most important document in the
multi-document set. The sentences in the most important document are aligned to
sentences in other documents to generate clusters of similar sentences. Second,
we generate K-shortest paths from the sentences in each cluster using a
word-graph structure. Finally, we select sentences from the set of shortest
paths generated from all the clusters employing a novel integer linear
programming (ILP) model with the objective of maximizing information content
and readability of the final summary. Our ILP model represents the shortest
paths as binary variables and considers the length of the path, information
score and linguistic quality score in the objective function. Experimental
results on the DUC 2004 and 2005 multi-document summarization datasets show
that our proposed approach outperforms all the baselines and state-of-the-art
extractive summarizers as measured by the ROUGE scores. Our method also
outperforms a recent abstractive summarization technique. In manual evaluation,
our approach also achieves promising results on informativeness and
readability.
| no_new_dataset | 0.947137 |
1609.07061 | Itay Hubara | Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv and
Yoshua Bengio | Quantized Neural Networks: Training Neural Networks with Low Precision
Weights and Activations | arXiv admin note: text overlap with arXiv:1602.02830 | null | null | null | cs.NE cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a method to train Quantized Neural Networks (QNNs) --- neural
networks with extremely low precision (e.g., 1-bit) weights and activations, at
run-time. At train-time the quantized weights and activations are used for
computing the parameter gradients. During the forward pass, QNNs drastically
reduce memory size and accesses, and replace most arithmetic operations with
bit-wise operations. As a result, power consumption is expected to be
drastically reduced. We trained QNNs over the MNIST, CIFAR-10, SVHN and
ImageNet datasets. The resulting QNNs achieve prediction accuracy comparable to
their 32-bit counterparts. For example, our quantized version of AlexNet with
1-bit weights and 2-bit activations achieves $51\%$ top-1 accuracy. Moreover,
we quantize the parameter gradients to 6-bits as well which enables gradients
computation using only bit-wise operation. Quantized recurrent neural networks
were tested over the Penn Treebank dataset, and achieved comparable accuracy as
their 32-bit counterparts using only 4-bits. Last but not least, we programmed
a binary matrix multiplication GPU kernel with which it is possible to run our
MNIST QNN 7 times faster than with an unoptimized GPU kernel, without suffering
any loss in classification accuracy. The QNN code is available online.
| [
{
"version": "v1",
"created": "Thu, 22 Sep 2016 16:48:03 GMT"
}
] | 2016-09-23T00:00:00 | [
[
"Hubara",
"Itay",
""
],
[
"Courbariaux",
"Matthieu",
""
],
[
"Soudry",
"Daniel",
""
],
[
"El-Yaniv",
"Ran",
""
],
[
"Bengio",
"Yoshua",
""
]
] | TITLE: Quantized Neural Networks: Training Neural Networks with Low Precision
Weights and Activations
ABSTRACT: We introduce a method to train Quantized Neural Networks (QNNs) --- neural
networks with extremely low precision (e.g., 1-bit) weights and activations, at
run-time. At train-time the quantized weights and activations are used for
computing the parameter gradients. During the forward pass, QNNs drastically
reduce memory size and accesses, and replace most arithmetic operations with
bit-wise operations. As a result, power consumption is expected to be
drastically reduced. We trained QNNs over the MNIST, CIFAR-10, SVHN and
ImageNet datasets. The resulting QNNs achieve prediction accuracy comparable to
their 32-bit counterparts. For example, our quantized version of AlexNet with
1-bit weights and 2-bit activations achieves $51\%$ top-1 accuracy. Moreover,
we quantize the parameter gradients to 6-bits as well which enables gradients
computation using only bit-wise operation. Quantized recurrent neural networks
were tested over the Penn Treebank dataset, and achieved comparable accuracy as
their 32-bit counterparts using only 4-bits. Last but not least, we programmed
a binary matrix multiplication GPU kernel with which it is possible to run our
MNIST QNN 7 times faster than with an unoptimized GPU kernel, without suffering
any loss in classification accuracy. The QNN code is available online.
| no_new_dataset | 0.943919 |
1609.07086 | Arvind Saibaba | Jiani Zhang, Arvind K. Saibaba, Misha Kilmer, Shuchin Aeron | A Randomized Tensor Singular Value Decomposition based on the t-product | null | null | null | null | math.NA cs.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The tensor Singular Value Decomposition (t-SVD) for third order tensors that
was proposed by Kilmer and Martin~\cite{2011kilmer} has been applied
successfully in many fields, such as computed tomography, facial recognition,
and video completion. In this paper, we propose a method that extends a
well-known randomized matrix method to the t-SVD. This method can produce a
factorization with similar properties to the t-SVD, but is more computationally
efficient on very large datasets. We present details of the algorithm,
theoretical results, and provide numerical results that show the promise of our
approach for compressing and analyzing datasets. We also present an improved
analysis of the randomized subspace iteration for matrices, which may be of
independent interest to the scientific community.
| [
{
"version": "v1",
"created": "Thu, 22 Sep 2016 17:55:21 GMT"
}
] | 2016-09-23T00:00:00 | [
[
"Zhang",
"Jiani",
""
],
[
"Saibaba",
"Arvind K.",
""
],
[
"Kilmer",
"Misha",
""
],
[
"Aeron",
"Shuchin",
""
]
] | TITLE: A Randomized Tensor Singular Value Decomposition based on the t-product
ABSTRACT: The tensor Singular Value Decomposition (t-SVD) for third order tensors that
was proposed by Kilmer and Martin~\cite{2011kilmer} has been applied
successfully in many fields, such as computed tomography, facial recognition,
and video completion. In this paper, we propose a method that extends a
well-known randomized matrix method to the t-SVD. This method can produce a
factorization with similar properties to the t-SVD, but is more computationally
efficient on very large datasets. We present details of the algorithm,
theoretical results, and provide numerical results that show the promise of our
approach for compressing and analyzing datasets. We also present an improved
analysis of the randomized subspace iteration for matrices, which may be of
independent interest to the scientific community.
| no_new_dataset | 0.95018 |
1512.02902 | Makarand Tapaswi | Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba,
Raquel Urtasun, Sanja Fidler | MovieQA: Understanding Stories in Movies through Question-Answering | CVPR 2016, Spotlight presentation. Benchmark @
http://movieqa.cs.toronto.edu/ Code @
https://github.com/makarandtapaswi/MovieQA_CVPR2016/ | null | null | null | cs.CV cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the MovieQA dataset which aims to evaluate automatic story
comprehension from both video and text. The dataset consists of 14,944
questions about 408 movies with high semantic diversity. The questions range
from simpler "Who" did "What" to "Whom", to "Why" and "How" certain events
occurred. Each question comes with a set of five possible answers; a correct
one and four deceiving answers provided by human annotators. Our dataset is
unique in that it contains multiple sources of information -- video clips,
plots, subtitles, scripts, and DVS. We analyze our data through various
statistics and methods. We further extend existing QA techniques to show that
question-answering with such open-ended semantics is hard. We make this data
set public along with an evaluation benchmark to encourage inspiring work in
this challenging domain.
| [
{
"version": "v1",
"created": "Wed, 9 Dec 2015 15:34:31 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Sep 2016 04:52:35 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Tapaswi",
"Makarand",
""
],
[
"Zhu",
"Yukun",
""
],
[
"Stiefelhagen",
"Rainer",
""
],
[
"Torralba",
"Antonio",
""
],
[
"Urtasun",
"Raquel",
""
],
[
"Fidler",
"Sanja",
""
]
] | TITLE: MovieQA: Understanding Stories in Movies through Question-Answering
ABSTRACT: We introduce the MovieQA dataset which aims to evaluate automatic story
comprehension from both video and text. The dataset consists of 14,944
questions about 408 movies with high semantic diversity. The questions range
from simpler "Who" did "What" to "Whom", to "Why" and "How" certain events
occurred. Each question comes with a set of five possible answers; a correct
one and four deceiving answers provided by human annotators. Our dataset is
unique in that it contains multiple sources of information -- video clips,
plots, subtitles, scripts, and DVS. We analyze our data through various
statistics and methods. We further extend existing QA techniques to show that
question-answering with such open-ended semantics is hard. We make this data
set public along with an evaluation benchmark to encourage inspiring work in
this challenging domain.
| new_dataset | 0.960025 |
1602.03426 | Aditya Joshi | Aditya Joshi, Pushpak Bhattacharyya, Mark James Carman | Automatic Sarcasm Detection: A Survey | This paper is likely to be submitted to ACM CSUR. This copy on arXiv
is to obtain feedback from stakeholders | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatic sarcasm detection is the task of predicting sarcasm in text. This
is a crucial step to sentiment analysis, considering prevalence and challenges
of sarcasm in sentiment-bearing text. Beginning with an approach that used
speech-based features, sarcasm detection has witnessed great interest from the
sentiment analysis community. This paper is the first known compilation of past
work in automatic sarcasm detection. We observe three milestones in the
research so far: semi-supervised pattern extraction to identify implicit
sentiment, use of hashtag-based supervision, and use of context beyond target
text. In this paper, we describe datasets, approaches, trends and issues in
sarcasm detection. We also discuss representative performance values, shared
tasks and pointers to future work, as given in prior works. In terms of
resources that could be useful for understanding state-of-the-art, the survey
presents several useful illustrations - most prominently, a table that
summarizes past papers along different dimensions such as features, annotation
techniques, data forms, etc.
| [
{
"version": "v1",
"created": "Wed, 10 Feb 2016 16:02:46 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Sep 2016 22:15:52 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Joshi",
"Aditya",
""
],
[
"Bhattacharyya",
"Pushpak",
""
],
[
"Carman",
"Mark James",
""
]
] | TITLE: Automatic Sarcasm Detection: A Survey
ABSTRACT: Automatic sarcasm detection is the task of predicting sarcasm in text. This
is a crucial step to sentiment analysis, considering prevalence and challenges
of sarcasm in sentiment-bearing text. Beginning with an approach that used
speech-based features, sarcasm detection has witnessed great interest from the
sentiment analysis community. This paper is the first known compilation of past
work in automatic sarcasm detection. We observe three milestones in the
research so far: semi-supervised pattern extraction to identify implicit
sentiment, use of hashtag-based supervision, and use of context beyond target
text. In this paper, we describe datasets, approaches, trends and issues in
sarcasm detection. We also discuss representative performance values, shared
tasks and pointers to future work, as given in prior works. In terms of
resources that could be useful for understanding state-of-the-art, the survey
presents several useful illustrations - most prominently, a table that
summarizes past papers along different dimensions such as features, annotation
techniques, data forms, etc.
| no_new_dataset | 0.938969 |
1606.05741 | Christian Jacobs | Christian T. Jacobs, Alexandros Avdis | Connecting web-based mapping services with scientific data repositories:
collaborative curation and retrieval of simulation data via a geospatial
interface | Submission withdrawn from the International Journal of Digital
Curation on 9 September 2016 in order to prepare a joint paper with
additional colleagues | null | null | null | cs.DL | http://creativecommons.org/licenses/by/4.0/ | Increasing quantities of scientific data are becoming readily accessible via
online repositories such as those provided by Figshare and Zenodo.
Geoscientific simulations in particular generate large quantities of data, with
several research groups studying many, often overlapping areas of the world.
When studying a particular area, being able to keep track of one's own
simulations as well as those of collaborators can be challenging. This paper
describes the design, implementation, and evaluation of a new tool for visually
cataloguing and retrieving data associated with a given geographical location
through a web-based Google Maps interface. Each data repository is pin-pointed
on the map with a marker based on the geographical location that the dataset
corresponds to. By clicking on the markers, users can quickly inspect the
metadata of the repositories and download the associated data files. The crux
of the approach lies in the ability to easily query and retrieve data from
multiple sources via a common interface. While many advances are being made in
terms of scientific data repositories, the development of this new tool has
uncovered several issues and limitations of the current state-of-the-art which
are discussed herein, along with some ideas for the future.
| [
{
"version": "v1",
"created": "Sat, 18 Jun 2016 11:46:10 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Sep 2016 19:56:01 GMT"
},
{
"version": "v3",
"created": "Wed, 21 Sep 2016 08:01:24 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Jacobs",
"Christian T.",
""
],
[
"Avdis",
"Alexandros",
""
]
] | TITLE: Connecting web-based mapping services with scientific data repositories:
collaborative curation and retrieval of simulation data via a geospatial
interface
ABSTRACT: Increasing quantities of scientific data are becoming readily accessible via
online repositories such as those provided by Figshare and Zenodo.
Geoscientific simulations in particular generate large quantities of data, with
several research groups studying many, often overlapping areas of the world.
When studying a particular area, being able to keep track of one's own
simulations as well as those of collaborators can be challenging. This paper
describes the design, implementation, and evaluation of a new tool for visually
cataloguing and retrieving data associated with a given geographical location
through a web-based Google Maps interface. Each data repository is pin-pointed
on the map with a marker based on the geographical location that the dataset
corresponds to. By clicking on the markers, users can quickly inspect the
metadata of the repositories and download the associated data files. The crux
of the approach lies in the ability to easily query and retrieve data from
multiple sources via a common interface. While many advances are being made in
terms of scientific data repositories, the development of this new tool has
uncovered several issues and limitations of the current state-of-the-art which
are discussed herein, along with some ideas for the future.
| no_new_dataset | 0.943867 |
1609.06265 | Faizan Javed | Janani Balaji, Faizan Javed, Mayank Kejriwal, Chris Min, Sam Sander
and Ozgur Ozturk | An Ensemble Blocking Scheme for Entity Resolution of Large and Sparse
Datasets | null | null | null | null | cs.AI cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Entity Resolution, also called record linkage or deduplication, refers to the
process of identifying and merging duplicate versions of the same entity into a
unified representation. The standard practice is to use a Rule based or Machine
Learning based model that compares entity pairs and assigns a score to
represent the pairs' Match/Non-Match status. However, performing an exhaustive
pair-wise comparison on all pairs of records leads to quadratic matcher
complexity and hence a Blocking step is performed before the Matching to group
similar entities into smaller blocks that the matcher can then examine
exhaustively. Several blocking schemes have been developed to efficiently and
effectively block the input dataset into manageable groups. At CareerBuilder
(CB), we perform deduplication on massive datasets of people profiles collected
from disparate sources with varying informational content. We observed that,
employing a single blocking technique did not cover the base for all possible
scenarios due to the multi-faceted nature of our data sources. In this paper,
we describe our ensemble approach to blocking that combines two different
blocking techniques to leverage their respective strengths.
| [
{
"version": "v1",
"created": "Tue, 20 Sep 2016 17:44:28 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Sep 2016 00:26:17 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Balaji",
"Janani",
""
],
[
"Javed",
"Faizan",
""
],
[
"Kejriwal",
"Mayank",
""
],
[
"Min",
"Chris",
""
],
[
"Sander",
"Sam",
""
],
[
"Ozturk",
"Ozgur",
""
]
] | TITLE: An Ensemble Blocking Scheme for Entity Resolution of Large and Sparse
Datasets
ABSTRACT: Entity Resolution, also called record linkage or deduplication, refers to the
process of identifying and merging duplicate versions of the same entity into a
unified representation. The standard practice is to use a Rule based or Machine
Learning based model that compares entity pairs and assigns a score to
represent the pairs' Match/Non-Match status. However, performing an exhaustive
pair-wise comparison on all pairs of records leads to quadratic matcher
complexity and hence a Blocking step is performed before the Matching to group
similar entities into smaller blocks that the matcher can then examine
exhaustively. Several blocking schemes have been developed to efficiently and
effectively block the input dataset into manageable groups. At CareerBuilder
(CB), we perform deduplication on massive datasets of people profiles collected
from disparate sources with varying informational content. We observed that,
employing a single blocking technique did not cover the base for all possible
scenarios due to the multi-faceted nature of our data sources. In this paper,
we describe our ensemble approach to blocking that combines two different
blocking techniques to leverage their respective strengths.
| no_new_dataset | 0.94887 |
1609.06335 | Anthony Gitter | Anthony Gitter, Furong Huang, Ragupathyraj Valluvan, Ernest Fraenkel,
Animashree Anandkumar | Unsupervised learning of transcriptional regulatory networks via latent
tree graphical models | 37 pages, 9 figures | null | null | null | q-bio.MN cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene expression is a readily-observed quantification of transcriptional
activity and cellular state that enables the recovery of the relationships
between regulators and their target genes. Reconstructing transcriptional
regulatory networks from gene expression data is a problem that has attracted
much attention, but previous work often makes the simplifying (but unrealistic)
assumption that regulator activity is represented by mRNA levels. We use a
latent tree graphical model to analyze gene expression without relying on
transcription factor expression as a proxy for regulator activity. The latent
tree model is a type of Markov random field that includes both observed gene
variables and latent (hidden) variables, which factorize on a Markov tree.
Through efficient unsupervised learning approaches, we determine which groups
of genes are co-regulated by hidden regulators and the activity levels of those
regulators. Post-processing annotates many of these discovered latent variables
as specific transcription factors or groups of transcription factors. Other
latent variables do not necessarily represent physical regulators but instead
reveal hidden structure in the gene expression such as shared biological
function. We apply the latent tree graphical model to a yeast stress response
dataset. In addition to novel predictions, such as condition-specific binding
of the transcription factor Msn4, our model recovers many known aspects of the
yeast regulatory network. These include groups of co-regulated genes,
condition-specific regulator activity, and combinatorial regulation among
transcription factors. The latent tree graphical model is a general approach
for analyzing gene expression data that requires no prior knowledge of which
possible regulators exist, regulator activity, or where transcription factors
physically bind.
| [
{
"version": "v1",
"created": "Tue, 20 Sep 2016 20:14:15 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Gitter",
"Anthony",
""
],
[
"Huang",
"Furong",
""
],
[
"Valluvan",
"Ragupathyraj",
""
],
[
"Fraenkel",
"Ernest",
""
],
[
"Anandkumar",
"Animashree",
""
]
] | TITLE: Unsupervised learning of transcriptional regulatory networks via latent
tree graphical models
ABSTRACT: Gene expression is a readily-observed quantification of transcriptional
activity and cellular state that enables the recovery of the relationships
between regulators and their target genes. Reconstructing transcriptional
regulatory networks from gene expression data is a problem that has attracted
much attention, but previous work often makes the simplifying (but unrealistic)
assumption that regulator activity is represented by mRNA levels. We use a
latent tree graphical model to analyze gene expression without relying on
transcription factor expression as a proxy for regulator activity. The latent
tree model is a type of Markov random field that includes both observed gene
variables and latent (hidden) variables, which factorize on a Markov tree.
Through efficient unsupervised learning approaches, we determine which groups
of genes are co-regulated by hidden regulators and the activity levels of those
regulators. Post-processing annotates many of these discovered latent variables
as specific transcription factors or groups of transcription factors. Other
latent variables do not necessarily represent physical regulators but instead
reveal hidden structure in the gene expression such as shared biological
function. We apply the latent tree graphical model to a yeast stress response
dataset. In addition to novel predictions, such as condition-specific binding
of the transcription factor Msn4, our model recovers many known aspects of the
yeast regulatory network. These include groups of co-regulated genes,
condition-specific regulator activity, and combinatorial regulation among
transcription factors. The latent tree graphical model is a general approach
for analyzing gene expression data that requires no prior knowledge of which
possible regulators exist, regulator activity, or where transcription factors
physically bind.
| no_new_dataset | 0.955194 |
1609.06380 | Yang Liu | Yang Liu and Sujian Li | Recognizing Implicit Discourse Relations via Repeated Reading: Neural
Networks with Multi-Level Attention | Accepted as long paper at EMNLP2016 | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recognizing implicit discourse relations is a challenging but important task
in the field of Natural Language Processing. For such a complex text processing
task, different from previous studies, we argue that it is necessary to
repeatedly read the arguments and dynamically exploit the efficient features
useful for recognizing discourse relations. To mimic the repeated reading
strategy, we propose the neural networks with multi-level attention (NNMA),
combining the attention mechanism and external memories to gradually fix the
attention on some specific words helpful to judging the discourse relations.
Experiments on the PDTB dataset show that our proposed method achieves the
state-of-art results. The visualization of the attention weights also
illustrates the progress that our model observes the arguments on each level
and progressively locates the important words.
| [
{
"version": "v1",
"created": "Tue, 20 Sep 2016 22:59:19 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Liu",
"Yang",
""
],
[
"Li",
"Sujian",
""
]
] | TITLE: Recognizing Implicit Discourse Relations via Repeated Reading: Neural
Networks with Multi-Level Attention
ABSTRACT: Recognizing implicit discourse relations is a challenging but important task
in the field of Natural Language Processing. For such a complex text processing
task, different from previous studies, we argue that it is necessary to
repeatedly read the arguments and dynamically exploit the efficient features
useful for recognizing discourse relations. To mimic the repeated reading
strategy, we propose the neural networks with multi-level attention (NNMA),
combining the attention mechanism and external memories to gradually fix the
attention on some specific words helpful to judging the discourse relations.
Experiments on the PDTB dataset show that our proposed method achieves the
state-of-art results. The visualization of the attention weights also
illustrates the progress that our model observes the arguments on each level
and progressively locates the important words.
| no_new_dataset | 0.948965 |
1609.06434 | Haoran Chen | Haoran Chen, Yanfeng Sun, Junbin Gao, Yongli Hu, Baocai Yin | Partial Least Squares Regression on Riemannian Manifolds and Its
Application in Classifications | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Partial least squares regression (PLSR) has been a popular technique to
explore the linear relationship between two datasets. However, most of
algorithm implementations of PLSR may only achieve a suboptimal solution
through an optimization on the Euclidean space. In this paper, we propose
several novel PLSR models on Riemannian manifolds and develop optimization
algorithms based on Riemannian geometry of manifolds. This algorithm can
calculate all the factors of PLSR globally to avoid suboptimal solutions. In a
number of experiments, we have demonstrated the benefits of applying the
proposed model and algorithm to a variety of learning tasks in pattern
recognition and object classification.
| [
{
"version": "v1",
"created": "Wed, 21 Sep 2016 06:48:07 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Chen",
"Haoran",
""
],
[
"Sun",
"Yanfeng",
""
],
[
"Gao",
"Junbin",
""
],
[
"Hu",
"Yongli",
""
],
[
"Yin",
"Baocai",
""
]
] | TITLE: Partial Least Squares Regression on Riemannian Manifolds and Its
Application in Classifications
ABSTRACT: Partial least squares regression (PLSR) has been a popular technique to
explore the linear relationship between two datasets. However, most of
algorithm implementations of PLSR may only achieve a suboptimal solution
through an optimization on the Euclidean space. In this paper, we propose
several novel PLSR models on Riemannian manifolds and develop optimization
algorithms based on Riemannian geometry of manifolds. This algorithm can
calculate all the factors of PLSR globally to avoid suboptimal solutions. In a
number of experiments, we have demonstrated the benefits of applying the
proposed model and algorithm to a variety of learning tasks in pattern
recognition and object classification.
| no_new_dataset | 0.949012 |
1609.06457 | Pin-Yu Chen | Pin-Yu Chen and Thibaut Gensollen and Alfred O. Hero III | AMOS: An Automated Model Order Selection Algorithm for Spectral Graph
Clustering | arXiv admin note: substantial text overlap with arXiv:1604.03159 | null | null | null | cs.SI cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the longstanding problems in spectral graph clustering (SGC) is the
so-called model order selection problem: automated selection of the correct
number of clusters. This is equivalent to the problem of finding the number of
connected components or communities in an undirected graph. In this paper, we
propose AMOS, an automated model order selection algorithm for SGC. Based on a
recent analysis of clustering reliability for SGC under the random
interconnection model, AMOS works by incrementally increasing the number of
clusters, estimating the quality of identified clusters, and providing a series
of clustering reliability tests. Consequently, AMOS outputs clusters of minimal
model order with statistical clustering reliability guarantees. Comparing to
three other automated graph clustering methods on real-world datasets, AMOS
shows superior performance in terms of multiple external and internal
clustering metrics.
| [
{
"version": "v1",
"created": "Wed, 21 Sep 2016 08:14:12 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Chen",
"Pin-Yu",
""
],
[
"Gensollen",
"Thibaut",
""
],
[
"Hero",
"Alfred O.",
"III"
]
] | TITLE: AMOS: An Automated Model Order Selection Algorithm for Spectral Graph
Clustering
ABSTRACT: One of the longstanding problems in spectral graph clustering (SGC) is the
so-called model order selection problem: automated selection of the correct
number of clusters. This is equivalent to the problem of finding the number of
connected components or communities in an undirected graph. In this paper, we
propose AMOS, an automated model order selection algorithm for SGC. Based on a
recent analysis of clustering reliability for SGC under the random
interconnection model, AMOS works by incrementally increasing the number of
clusters, estimating the quality of identified clusters, and providing a series
of clustering reliability tests. Consequently, AMOS outputs clusters of minimal
model order with statistical clustering reliability guarantees. Comparing to
three other automated graph clustering methods on real-world datasets, AMOS
shows superior performance in terms of multiple external and internal
clustering metrics.
| no_new_dataset | 0.953362 |
1609.06532 | Kar Wai Lim | Kar Wai Lim and Wray Buntine | Bibliographic Analysis on Research Publications using Authors,
Categorical Labels and the Citation Network | Preprint for Journal Machine Learning | Machine Learning 103(2):185-213, 2016 | 10.1007/s10994-016-5554-z | null | cs.DL cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bibliographic analysis considers the author's research areas, the citation
network and the paper content among other things. In this paper, we combine
these three in a topic model that produces a bibliographic model of authors,
topics and documents, using a nonparametric extension of a combination of the
Poisson mixed-topic link model and the author-topic model. This gives rise to
the Citation Network Topic Model (CNTM). We propose a novel and efficient
inference algorithm for the CNTM to explore subsets of research publications
from CiteSeerX. The publication datasets are organised into three corpora,
totalling to about 168k publications with about 62k authors. The queried
datasets are made available online. In three publicly available corpora in
addition to the queried datasets, our proposed model demonstrates an improved
performance in both model fitting and document clustering, compared to several
baselines. Moreover, our model allows extraction of additional useful knowledge
from the corpora, such as the visualisation of the author-topics network.
Additionally, we propose a simple method to incorporate supervision into topic
modelling to achieve further improvement on the clustering task.
| [
{
"version": "v1",
"created": "Wed, 21 Sep 2016 12:44:37 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Lim",
"Kar Wai",
""
],
[
"Buntine",
"Wray",
""
]
] | TITLE: Bibliographic Analysis on Research Publications using Authors,
Categorical Labels and the Citation Network
ABSTRACT: Bibliographic analysis considers the author's research areas, the citation
network and the paper content among other things. In this paper, we combine
these three in a topic model that produces a bibliographic model of authors,
topics and documents, using a nonparametric extension of a combination of the
Poisson mixed-topic link model and the author-topic model. This gives rise to
the Citation Network Topic Model (CNTM). We propose a novel and efficient
inference algorithm for the CNTM to explore subsets of research publications
from CiteSeerX. The publication datasets are organised into three corpora,
totalling to about 168k publications with about 62k authors. The queried
datasets are made available online. In three publicly available corpora in
addition to the queried datasets, our proposed model demonstrates an improved
performance in both model fitting and document clustering, compared to several
baselines. Moreover, our model allows extraction of additional useful knowledge
from the corpora, such as the visualisation of the author-topics network.
Additionally, we propose a simple method to incorporate supervision into topic
modelling to achieve further improvement on the clustering task.
| no_new_dataset | 0.949995 |
1609.06570 | Guillaume Lemaitre | Guillaume Lemaitre and Fernando Nogueira and Christos K. Aridas | Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced
Datasets in Machine Learning | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Imbalanced-learn is an open-source python toolbox aiming at providing a wide
range of methods to cope with the problem of imbalanced dataset frequently
encountered in machine learning and pattern recognition. The implemented
state-of-the-art methods can be categorized into 4 groups: (i) under-sampling,
(ii) over-sampling, (iii) combination of over- and under-sampling, and (iv)
ensemble learning methods. The proposed toolbox only depends on numpy, scipy,
and scikit-learn and is distributed under MIT license. Furthermore, it is fully
compatible with scikit-learn and is part of the scikit-learn-contrib supported
project. Documentation, unit tests as well as integration tests are provided to
ease usage and contribution. The toolbox is publicly available in GitHub:
https://github.com/scikit-learn-contrib/imbalanced-learn.
| [
{
"version": "v1",
"created": "Wed, 21 Sep 2016 14:16:14 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Lemaitre",
"Guillaume",
""
],
[
"Nogueira",
"Fernando",
""
],
[
"Aridas",
"Christos K.",
""
]
] | TITLE: Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced
Datasets in Machine Learning
ABSTRACT: Imbalanced-learn is an open-source python toolbox aiming at providing a wide
range of methods to cope with the problem of imbalanced dataset frequently
encountered in machine learning and pattern recognition. The implemented
state-of-the-art methods can be categorized into 4 groups: (i) under-sampling,
(ii) over-sampling, (iii) combination of over- and under-sampling, and (iv)
ensemble learning methods. The proposed toolbox only depends on numpy, scipy,
and scikit-learn and is distributed under MIT license. Furthermore, it is fully
compatible with scikit-learn and is part of the scikit-learn-contrib supported
project. Documentation, unit tests as well as integration tests are provided to
ease usage and contribution. The toolbox is publicly available in GitHub:
https://github.com/scikit-learn-contrib/imbalanced-learn.
| no_new_dataset | 0.940735 |
1609.06604 | Filippo Arcadu | Filippo Arcadu, Jakob Vogel, Marco Stampanoni and Federica Marone | Improving analytical tomographic reconstructions through consistency
conditions | 16 pages, 12 figures | null | null | null | physics.med-ph cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work introduces and characterizes a fast parameterless filter based on
the Helgason-Ludwig consistency conditions, used to improve the accuracy of
analytical reconstructions of tomographic undersampled datasets. The filter,
acting in the Radon domain, extrapolates intermediate projections between those
existing. The resulting sinogram, doubled in views, is then reconstructed by a
standard analytical method. Experiments with simulated data prove that the
peak-signal-to-noise ratio of the results computed by filtered backprojection
is improved up to 5-6 dB, if the filter is used prior to reconstruction.
| [
{
"version": "v1",
"created": "Wed, 21 Sep 2016 15:34:39 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Arcadu",
"Filippo",
""
],
[
"Vogel",
"Jakob",
""
],
[
"Stampanoni",
"Marco",
""
],
[
"Marone",
"Federica",
""
]
] | TITLE: Improving analytical tomographic reconstructions through consistency
conditions
ABSTRACT: This work introduces and characterizes a fast parameterless filter based on
the Helgason-Ludwig consistency conditions, used to improve the accuracy of
analytical reconstructions of tomographic undersampled datasets. The filter,
acting in the Radon domain, extrapolates intermediate projections between those
existing. The resulting sinogram, doubled in views, is then reconstructed by a
standard analytical method. Experiments with simulated data prove that the
peak-signal-to-noise ratio of the results computed by filtered backprojection
is improved up to 5-6 dB, if the filter is used prior to reconstruction.
| no_new_dataset | 0.95297 |
1609.06612 | Edip Demirbilek | Edip Demirbilek and Jean-Charles Gr\'egoire | Multimedia Communication Quality Assessment Testbeds | 9 pages, 5 figures. this has not been submitted to any conference
yet. however some part of it would be presented in GStreamer Conf 2016. As
the GStreamer conf requires only an abstract submission, we though it would
be better to share the actual content via arxiv | null | null | null | cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We make an intensive use of multimedia frameworks in our research on modeling
the perceived quality estimation in streaming services and real-time
communications. In our preliminary work, we have used the VLC VOD software to
generate reference audiovisual files with various degree of coding and network
degradations. We have successfully built machine learning based models on the
subjective quality dataset we have generated using these files. However,
imperfections in the dataset introduced by the multimedia framework we have
used prevented us from achieving the full potential of these models.
In order to develop better models, we have re-created our end-to-end
multimedia pipeline using the GStreamer framework for audio and video
streaming. A GStreamer based pipeline proved to be significantly more robust to
network degradations than the VLC VOD framework and allowed us to stream a
video flow at a loss rate up to 5\% packet very easily. GStreamer has also
enabled us to collect the relevant RTCP statistics that proved to be more
accurate than network-deduced information. This dataset is free to the public.
The accuracy of the statistics eventually helped us to generate better
performing perceived quality estimation models.
In this paper, we present the implementation of these VLC and GStreamer-based
multimedia communication quality assessment testbeds with the references to
their publicly available code bases.
| [
{
"version": "v1",
"created": "Wed, 21 Sep 2016 15:59:59 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Demirbilek",
"Edip",
""
],
[
"Grégoire",
"Jean-Charles",
""
]
] | TITLE: Multimedia Communication Quality Assessment Testbeds
ABSTRACT: We make an intensive use of multimedia frameworks in our research on modeling
the perceived quality estimation in streaming services and real-time
communications. In our preliminary work, we have used the VLC VOD software to
generate reference audiovisual files with various degree of coding and network
degradations. We have successfully built machine learning based models on the
subjective quality dataset we have generated using these files. However,
imperfections in the dataset introduced by the multimedia framework we have
used prevented us from achieving the full potential of these models.
In order to develop better models, we have re-created our end-to-end
multimedia pipeline using the GStreamer framework for audio and video
streaming. A GStreamer based pipeline proved to be significantly more robust to
network degradations than the VLC VOD framework and allowed us to stream a
video flow at a loss rate up to 5\% packet very easily. GStreamer has also
enabled us to collect the relevant RTCP statistics that proved to be more
accurate than network-deduced information. This dataset is free to the public.
The accuracy of the statistics eventually helped us to generate better
performing perceived quality estimation models.
In this paper, we present the implementation of these VLC and GStreamer-based
multimedia communication quality assessment testbeds with the references to
their publicly available code bases.
| no_new_dataset | 0.824462 |
1609.06647 | Oriol Vinyals | Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan | Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning
Challenge | arXiv admin note: substantial text overlap with arXiv:1411.4555 | IEEE Transactions on Pattern Analysis and Machine Intelligence (
Volume: PP, Issue: 99 , July 2016 ) | 10.1109/TPAMI.2016.2587640 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatically describing the content of an image is a fundamental problem in
artificial intelligence that connects computer vision and natural language
processing. In this paper, we present a generative model based on a deep
recurrent architecture that combines recent advances in computer vision and
machine translation and that can be used to generate natural sentences
describing an image. The model is trained to maximize the likelihood of the
target description sentence given the training image. Experiments on several
datasets show the accuracy of the model and the fluency of the language it
learns solely from image descriptions. Our model is often quite accurate, which
we verify both qualitatively and quantitatively. Finally, given the recent
surge of interest in this task, a competition was organized in 2015 using the
newly released COCO dataset. We describe and analyze the various improvements
we applied to our own baseline and show the resulting performance in the
competition, which we won ex-aequo with a team from Microsoft Research, and
provide an open source implementation in TensorFlow.
| [
{
"version": "v1",
"created": "Wed, 21 Sep 2016 17:40:57 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Vinyals",
"Oriol",
""
],
[
"Toshev",
"Alexander",
""
],
[
"Bengio",
"Samy",
""
],
[
"Erhan",
"Dumitru",
""
]
] | TITLE: Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning
Challenge
ABSTRACT: Automatically describing the content of an image is a fundamental problem in
artificial intelligence that connects computer vision and natural language
processing. In this paper, we present a generative model based on a deep
recurrent architecture that combines recent advances in computer vision and
machine translation and that can be used to generate natural sentences
describing an image. The model is trained to maximize the likelihood of the
target description sentence given the training image. Experiments on several
datasets show the accuracy of the model and the fluency of the language it
learns solely from image descriptions. Our model is often quite accurate, which
we verify both qualitatively and quantitatively. Finally, given the recent
surge of interest in this task, a competition was organized in 2015 using the
newly released COCO dataset. We describe and analyze the various improvements
we applied to our own baseline and show the resulting performance in the
competition, which we won ex-aequo with a team from Microsoft Research, and
provide an open source implementation in TensorFlow.
| new_dataset | 0.537929 |
1609.06653 | Yi Zhu | Yi Zhu and Shawn Newsam | Land Use Classification using Convolutional Neural Networks Applied to
Ground-Level Images | ACM SIGSPATIAL 2015, Best Poster Award | null | null | null | cs.CV cs.CY cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Land use mapping is a fundamental yet challenging task in geographic science.
In contrast to land cover mapping, it is generally not possible using overhead
imagery. The recent, explosive growth of online geo-referenced photo
collections suggests an alternate approach to geographic knowledge discovery.
In this work, we present a general framework that uses ground-level images from
Flickr for land use mapping. Our approach benefits from several novel aspects.
First, we address the nosiness of the online photo collections, such as
imprecise geolocation and uneven spatial distribution, by performing location
and indoor/outdoor filtering, and semi- supervised dataset augmentation. Our
indoor/outdoor classifier achieves state-of-the-art performance on several
bench- mark datasets and approaches human-level accuracy. Second, we utilize
high-level semantic image features extracted using deep learning, specifically
convolutional neural net- works, which allow us to achieve upwards of 76%
accuracy on a challenging eight class land use mapping problem.
| [
{
"version": "v1",
"created": "Wed, 21 Sep 2016 18:01:24 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Zhu",
"Yi",
""
],
[
"Newsam",
"Shawn",
""
]
] | TITLE: Land Use Classification using Convolutional Neural Networks Applied to
Ground-Level Images
ABSTRACT: Land use mapping is a fundamental yet challenging task in geographic science.
In contrast to land cover mapping, it is generally not possible using overhead
imagery. The recent, explosive growth of online geo-referenced photo
collections suggests an alternate approach to geographic knowledge discovery.
In this work, we present a general framework that uses ground-level images from
Flickr for land use mapping. Our approach benefits from several novel aspects.
First, we address the nosiness of the online photo collections, such as
imprecise geolocation and uneven spatial distribution, by performing location
and indoor/outdoor filtering, and semi- supervised dataset augmentation. Our
indoor/outdoor classifier achieves state-of-the-art performance on several
bench- mark datasets and approaches human-level accuracy. Second, we utilize
high-level semantic image features extracted using deep learning, specifically
convolutional neural net- works, which allow us to achieve upwards of 76%
accuracy on a challenging eight class land use mapping problem.
| no_new_dataset | 0.954984 |
1609.06657 | Andrew Shin | Andrew Shin, Yoshitaka Ushiku, Tatsuya Harada | The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question
Answering (FSVQA) | null | null | null | null | cs.CV cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visual Question Answering (VQA) task has showcased a new stage of interaction
between language and vision, two of the most pivotal components of artificial
intelligence. However, it has mostly focused on generating short and repetitive
answers, mostly single words, which fall short of rich linguistic capabilities
of humans. We introduce Full-Sentence Visual Question Answering (FSVQA)
dataset, consisting of nearly 1 million pairs of questions and full-sentence
answers for images, built by applying a number of rule-based natural language
processing techniques to original VQA dataset and captions in the MS COCO
dataset. This poses many additional complexities to conventional VQA task, and
we provide a baseline for approaching and evaluating the task, on top of which
we invite the research community to build further improvements.
| [
{
"version": "v1",
"created": "Wed, 21 Sep 2016 18:12:04 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Shin",
"Andrew",
""
],
[
"Ushiku",
"Yoshitaka",
""
],
[
"Harada",
"Tatsuya",
""
]
] | TITLE: The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question
Answering (FSVQA)
ABSTRACT: Visual Question Answering (VQA) task has showcased a new stage of interaction
between language and vision, two of the most pivotal components of artificial
intelligence. However, it has mostly focused on generating short and repetitive
answers, mostly single words, which fall short of rich linguistic capabilities
of humans. We introduce Full-Sentence Visual Question Answering (FSVQA)
dataset, consisting of nearly 1 million pairs of questions and full-sentence
answers for images, built by applying a number of rule-based natural language
processing techniques to original VQA dataset and captions in the MS COCO
dataset. This poses many additional complexities to conventional VQA task, and
we provide a baseline for approaching and evaluating the task, on top of which
we invite the research community to build further improvements.
| new_dataset | 0.962603 |
1609.06668 | Ziyue Xu | Mario Buty, Ziyue Xu, Mingchen Gao, Ulas Bagci, Aaron Wu, and Daniel
J. Mollura | Characterization of Lung Nodule Malignancy using Hybrid Shape and
Appearance Features | Accepted to MICCAI 2016 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computed tomography imaging is a standard modality for detecting and
assessing lung cancer. In order to evaluate the malignancy of lung nodules,
clinical practice often involves expert qualitative ratings on several criteria
describing a nodule's appearance and shape. Translating these features for
computer-aided diagnostics is challenging due to their subjective nature and
the difficulties in gaining a complete description. In this paper, we propose a
computerized approach to quantitatively evaluate both appearance distinctions
and 3D surface variations. Nodule shape was modeled and parameterized using
spherical harmonics, and appearance features were extracted using deep
convolutional neural networks. Both sets of features were combined to estimate
the nodule malignancy using a random forest classifier. The proposed algorithm
was tested on the publicly available Lung Image Database Consortium dataset,
achieving high accuracy. By providing lung nodule characterization, this method
can provide a robust alternative reference opinion for lung cancer diagnosis.
| [
{
"version": "v1",
"created": "Wed, 21 Sep 2016 18:33:56 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Buty",
"Mario",
""
],
[
"Xu",
"Ziyue",
""
],
[
"Gao",
"Mingchen",
""
],
[
"Bagci",
"Ulas",
""
],
[
"Wu",
"Aaron",
""
],
[
"Mollura",
"Daniel J.",
""
]
] | TITLE: Characterization of Lung Nodule Malignancy using Hybrid Shape and
Appearance Features
ABSTRACT: Computed tomography imaging is a standard modality for detecting and
assessing lung cancer. In order to evaluate the malignancy of lung nodules,
clinical practice often involves expert qualitative ratings on several criteria
describing a nodule's appearance and shape. Translating these features for
computer-aided diagnostics is challenging due to their subjective nature and
the difficulties in gaining a complete description. In this paper, we propose a
computerized approach to quantitatively evaluate both appearance distinctions
and 3D surface variations. Nodule shape was modeled and parameterized using
spherical harmonics, and appearance features were extracted using deep
convolutional neural networks. Both sets of features were combined to estimate
the nodule malignancy using a random forest classifier. The proposed algorithm
was tested on the publicly available Lung Image Database Consortium dataset,
achieving high accuracy. By providing lung nodule characterization, this method
can provide a robust alternative reference opinion for lung cancer diagnosis.
| no_new_dataset | 0.955527 |
1609.06676 | Li Sun | Li Sun, Steven Versteeg, Serdar Boztas and Asha Rao | Detecting Anomalous User Behavior Using an Extended Isolation Forest
Algorithm: An Enterprise Case Study | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Anomalous user behavior detection is the core component of many information
security systems, such as intrusion detection, insider threat detection and
authentication systems. Anomalous behavior will raise an alarm to the system
administrator and can be further combined with other information to determine
whether it constitutes an unauthorised or malicious use of a resource. This
paper presents an anomalous user behaviour detection framework that applies an
extended version of Isolation Forest algorithm. Our method is fast and scalable
and does not require example anomalies in the training data set. We apply our
method to an enterprise dataset. The experimental results show that the system
is able to isolate anomalous instances from the baseline user model using a
single feature or combined features.
| [
{
"version": "v1",
"created": "Wed, 21 Sep 2016 18:44:48 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Sun",
"Li",
""
],
[
"Versteeg",
"Steven",
""
],
[
"Boztas",
"Serdar",
""
],
[
"Rao",
"Asha",
""
]
] | TITLE: Detecting Anomalous User Behavior Using an Extended Isolation Forest
Algorithm: An Enterprise Case Study
ABSTRACT: Anomalous user behavior detection is the core component of many information
security systems, such as intrusion detection, insider threat detection and
authentication systems. Anomalous behavior will raise an alarm to the system
administrator and can be further combined with other information to determine
whether it constitutes an unauthorised or malicious use of a resource. This
paper presents an anomalous user behaviour detection framework that applies an
extended version of Isolation Forest algorithm. Our method is fast and scalable
and does not require example anomalies in the training data set. We apply our
method to an enterprise dataset. The experimental results show that the system
is able to isolate anomalous instances from the baseline user model using a
single feature or combined features.
| no_new_dataset | 0.939637 |
1609.06686 | Sebastian Ruder | Sebastian Ruder, Parsa Ghaffari, John G. Breslin | Character-level and Multi-channel Convolutional Neural Networks for
Large-scale Authorship Attribution | 9 pages, 5 figures, 3 tables | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Convolutional neural networks (CNNs) have demonstrated superior capability
for extracting information from raw signals in computer vision. Recently,
character-level and multi-channel CNNs have exhibited excellent performance for
sentence classification tasks. We apply CNNs to large-scale authorship
attribution, which aims to determine an unknown text's author among many
candidate authors, motivated by their ability to process character-level
signals and to differentiate between a large number of classes, while making
fast predictions in comparison to state-of-the-art approaches. We extensively
evaluate CNN-based approaches that leverage word and character channels and
compare them against state-of-the-art methods for a large range of author
numbers, shedding new light on traditional approaches. We show that
character-level CNNs outperform the state-of-the-art on four out of five
datasets in different domains. Additionally, we present the first application
of authorship attribution to reddit.
| [
{
"version": "v1",
"created": "Wed, 21 Sep 2016 19:08:15 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Ruder",
"Sebastian",
""
],
[
"Ghaffari",
"Parsa",
""
],
[
"Breslin",
"John G.",
""
]
] | TITLE: Character-level and Multi-channel Convolutional Neural Networks for
Large-scale Authorship Attribution
ABSTRACT: Convolutional neural networks (CNNs) have demonstrated superior capability
for extracting information from raw signals in computer vision. Recently,
character-level and multi-channel CNNs have exhibited excellent performance for
sentence classification tasks. We apply CNNs to large-scale authorship
attribution, which aims to determine an unknown text's author among many
candidate authors, motivated by their ability to process character-level
signals and to differentiate between a large number of classes, while making
fast predictions in comparison to state-of-the-art approaches. We extensively
evaluate CNN-based approaches that leverage word and character channels and
compare them against state-of-the-art methods for a large range of author
numbers, shedding new light on traditional approaches. We show that
character-level CNNs outperform the state-of-the-art on four out of five
datasets in different domains. Additionally, we present the first application
of authorship attribution to reddit.
| no_new_dataset | 0.951863 |
1609.06694 | Aayush Bansal | Aayush Bansal, Xinlei Chen, Bryan Russell, Abhinav Gupta, Deva Ramanan | PixelNet: Towards a General Pixel-level Architecture | null | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore architectures for general pixel-level prediction problems, from
low-level edge detection to mid-level surface normal estimation to high-level
semantic segmentation. Convolutional predictors, such as the
fully-convolutional network (FCN), have achieved remarkable success by
exploiting the spatial redundancy of neighboring pixels through convolutional
processing. Though computationally efficient, we point out that such approaches
are not statistically efficient during learning precisely because spatial
redundancy limits the information learned from neighboring pixels. We
demonstrate that (1) stratified sampling allows us to add diversity during
batch updates and (2) sampled multi-scale features allow us to explore more
nonlinear predictors (multiple fully-connected layers followed by ReLU) that
improve overall accuracy. Finally, our objective is to show how a architecture
can get performance better than (or comparable to) the architectures designed
for a particular task. Interestingly, our single architecture produces
state-of-the-art results for semantic segmentation on PASCAL-Context, surface
normal estimation on NYUDv2 dataset, and edge detection on BSDS without
contextual post-processing.
| [
{
"version": "v1",
"created": "Wed, 21 Sep 2016 19:32:46 GMT"
}
] | 2016-09-22T00:00:00 | [
[
"Bansal",
"Aayush",
""
],
[
"Chen",
"Xinlei",
""
],
[
"Russell",
"Bryan",
""
],
[
"Gupta",
"Abhinav",
""
],
[
"Ramanan",
"Deva",
""
]
] | TITLE: PixelNet: Towards a General Pixel-level Architecture
ABSTRACT: We explore architectures for general pixel-level prediction problems, from
low-level edge detection to mid-level surface normal estimation to high-level
semantic segmentation. Convolutional predictors, such as the
fully-convolutional network (FCN), have achieved remarkable success by
exploiting the spatial redundancy of neighboring pixels through convolutional
processing. Though computationally efficient, we point out that such approaches
are not statistically efficient during learning precisely because spatial
redundancy limits the information learned from neighboring pixels. We
demonstrate that (1) stratified sampling allows us to add diversity during
batch updates and (2) sampled multi-scale features allow us to explore more
nonlinear predictors (multiple fully-connected layers followed by ReLU) that
improve overall accuracy. Finally, our objective is to show how a architecture
can get performance better than (or comparable to) the architectures designed
for a particular task. Interestingly, our single architecture produces
state-of-the-art results for semantic segmentation on PASCAL-Context, surface
normal estimation on NYUDv2 dataset, and edge detection on BSDS without
contextual post-processing.
| no_new_dataset | 0.946498 |
1406.2431 | Oren Anava | Oren Anava, Shahar Golan, Nadav Golbandi, Zohar Karnin, Ronny Lempel,
Oleg Rokhlenko, Oren Somekh | Budget-Constrained Item Cold-Start Handling in Collaborative Filtering
Recommenders via Optimal Design | 11 pages, 2 figures | null | null | null | cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is well known that collaborative filtering (CF) based recommender systems
provide better modeling of users and items associated with considerable rating
history. The lack of historical ratings results in the user and the item
cold-start problems. The latter is the main focus of this work. Most of the
current literature addresses this problem by integrating content-based
recommendation techniques to model the new item. However, in many cases such
content is not available, and the question arises is whether this problem can
be mitigated using CF techniques only. We formalize this problem as an
optimization problem: given a new item, a pool of available users, and a budget
constraint, select which users to assign with the task of rating the new item
in order to minimize the prediction error of our model. We show that the
objective function is monotone-supermodular, and propose efficient optimal
design based algorithms that attain an approximation to its optimum. Our
findings are verified by an empirical study using the Netflix dataset, where
the proposed algorithms outperform several baselines for the problem at hand.
| [
{
"version": "v1",
"created": "Tue, 10 Jun 2014 06:17:23 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Nov 2014 21:10:43 GMT"
},
{
"version": "v3",
"created": "Tue, 20 Sep 2016 09:51:02 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Anava",
"Oren",
""
],
[
"Golan",
"Shahar",
""
],
[
"Golbandi",
"Nadav",
""
],
[
"Karnin",
"Zohar",
""
],
[
"Lempel",
"Ronny",
""
],
[
"Rokhlenko",
"Oleg",
""
],
[
"Somekh",
"Oren",
""
]
] | TITLE: Budget-Constrained Item Cold-Start Handling in Collaborative Filtering
Recommenders via Optimal Design
ABSTRACT: It is well known that collaborative filtering (CF) based recommender systems
provide better modeling of users and items associated with considerable rating
history. The lack of historical ratings results in the user and the item
cold-start problems. The latter is the main focus of this work. Most of the
current literature addresses this problem by integrating content-based
recommendation techniques to model the new item. However, in many cases such
content is not available, and the question arises is whether this problem can
be mitigated using CF techniques only. We formalize this problem as an
optimization problem: given a new item, a pool of available users, and a budget
constraint, select which users to assign with the task of rating the new item
in order to minimize the prediction error of our model. We show that the
objective function is monotone-supermodular, and propose efficient optimal
design based algorithms that attain an approximation to its optimum. Our
findings are verified by an empirical study using the Netflix dataset, where
the proposed algorithms outperform several baselines for the problem at hand.
| no_new_dataset | 0.946843 |
1505.04870 | Bryan Plummer | Bryan A. Plummer, Liwei Wang, Chris M. Cervantes, Juan C. Caicedo,
Julia Hockenmaier, and Svetlana Lazebnik | Flickr30k Entities: Collecting Region-to-Phrase Correspondences for
Richer Image-to-Sentence Models | null | null | null | null | cs.CV cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Flickr30k dataset has become a standard benchmark for sentence-based
image description. This paper presents Flickr30k Entities, which augments the
158k captions from Flickr30k with 244k coreference chains, linking mentions of
the same entities across different captions for the same image, and associating
them with 276k manually annotated bounding boxes. Such annotations are
essential for continued progress in automatic image description and grounded
language understanding. They enable us to define a new benchmark for
localization of textual entity mentions in an image. We present a strong
baseline for this task that combines an image-text embedding, detectors for
common objects, a color classifier, and a bias towards selecting larger
objects. While our baseline rivals in accuracy more complex state-of-the-art
models, we show that its gains cannot be easily parlayed into improvements on
such tasks as image-sentence retrieval, thus underlining the limitations of
current methods and the need for further research.
| [
{
"version": "v1",
"created": "Tue, 19 May 2015 04:46:03 GMT"
},
{
"version": "v2",
"created": "Mon, 5 Oct 2015 22:17:45 GMT"
},
{
"version": "v3",
"created": "Fri, 15 Apr 2016 14:58:37 GMT"
},
{
"version": "v4",
"created": "Mon, 19 Sep 2016 20:20:42 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Plummer",
"Bryan A.",
""
],
[
"Wang",
"Liwei",
""
],
[
"Cervantes",
"Chris M.",
""
],
[
"Caicedo",
"Juan C.",
""
],
[
"Hockenmaier",
"Julia",
""
],
[
"Lazebnik",
"Svetlana",
""
]
] | TITLE: Flickr30k Entities: Collecting Region-to-Phrase Correspondences for
Richer Image-to-Sentence Models
ABSTRACT: The Flickr30k dataset has become a standard benchmark for sentence-based
image description. This paper presents Flickr30k Entities, which augments the
158k captions from Flickr30k with 244k coreference chains, linking mentions of
the same entities across different captions for the same image, and associating
them with 276k manually annotated bounding boxes. Such annotations are
essential for continued progress in automatic image description and grounded
language understanding. They enable us to define a new benchmark for
localization of textual entity mentions in an image. We present a strong
baseline for this task that combines an image-text embedding, detectors for
common objects, a color classifier, and a bias towards selecting larger
objects. While our baseline rivals in accuracy more complex state-of-the-art
models, we show that its gains cannot be easily parlayed into improvements on
such tasks as image-sentence retrieval, thus underlining the limitations of
current methods and the need for further research.
| no_new_dataset | 0.935524 |
1509.04491 | Philip Schniter | Evan Byrne and Philip Schniter | Sparse Multinomial Logistic Regression via Approximate Message Passing | null | null | 10.1109/TSP.2016.2593691 | null | cs.IT math.IT stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For the problem of multi-class linear classification and feature selection,
we propose approximate message passing approaches to sparse multinomial
logistic regression (MLR). First, we propose two algorithms based on the Hybrid
Generalized Approximate Message Passing (HyGAMP) framework: one finds the
maximum a posteriori (MAP) linear classifier and the other finds an
approximation of the test-error-rate minimizing linear classifier. Then we
design computationally simplified variants of these two algorithms. Next, we
detail methods to tune the hyperparameters of their assumed statistical models
using Stein's unbiased risk estimate (SURE) and expectation-maximization (EM),
respectively. Finally, using both synthetic and real-world datasets, we
demonstrate improved error-rate and runtime performance relative to existing
state-of-the-art approaches to sparse MLR.
| [
{
"version": "v1",
"created": "Tue, 15 Sep 2015 11:08:33 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Jun 2016 19:11:23 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Byrne",
"Evan",
""
],
[
"Schniter",
"Philip",
""
]
] | TITLE: Sparse Multinomial Logistic Regression via Approximate Message Passing
ABSTRACT: For the problem of multi-class linear classification and feature selection,
we propose approximate message passing approaches to sparse multinomial
logistic regression (MLR). First, we propose two algorithms based on the Hybrid
Generalized Approximate Message Passing (HyGAMP) framework: one finds the
maximum a posteriori (MAP) linear classifier and the other finds an
approximation of the test-error-rate minimizing linear classifier. Then we
design computationally simplified variants of these two algorithms. Next, we
detail methods to tune the hyperparameters of their assumed statistical models
using Stein's unbiased risk estimate (SURE) and expectation-maximization (EM),
respectively. Finally, using both synthetic and real-world datasets, we
demonstrate improved error-rate and runtime performance relative to existing
state-of-the-art approaches to sparse MLR.
| no_new_dataset | 0.949153 |
1509.08970 | Priyadarshini Panda | Priyadarshini Panda, Swagath Venkataramani, Abhronil Sengupta, Anand
Raghunathan and Kaushik Roy | Energy-Efficient Object Detection using Semantic Decomposition | 10 pages, 13 figures, 3 algorithms, Submitted to IEEE TVLSI(Under
Review) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine-learning algorithms offer immense possibilities in the development of
several cognitive applications. In fact, large scale machine-learning
classifiers now represent the state-of-the-art in a wide range of object
detection/classification problems. However, the network complexities of
large-scale classifiers present them as one of the most challenging and energy
intensive workloads across the computing spectrum. In this paper, we present a
new approach to optimize energy efficiency of object detection tasks using
semantic decomposition to build a hierarchical classification framework. We
observe that certain semantic information like color/texture are common across
various images in real-world datasets for object detection applications. We
exploit these common semantic features to distinguish the objects of interest
from the remaining inputs (non-objects of interest) in a dataset at a lower
computational effort. We propose a 2-stage hierarchical classification
framework, with increasing levels of complexity, wherein the first stage is
trained to recognize the broad representative semantic features relevant to the
object of interest. The first stage rejects the input instances that do not
have the representative features and passes only the relevant instances to the
second stage. Our methodology thus allows us to reject certain information at
lower complexity and utilize the full computational effort of a network only on
a smaller fraction of inputs to perform detection. We use color and texture as
distinctive traits to carry out several experiments for object detection. Our
experiments on the Caltech101/CIFAR10 dataset show that the proposed method
yields 1.93x/1.46x improvement in average energy, respectively, over the
traditional single classifier model.
| [
{
"version": "v1",
"created": "Tue, 29 Sep 2015 22:56:33 GMT"
},
{
"version": "v2",
"created": "Tue, 12 Apr 2016 23:21:51 GMT"
},
{
"version": "v3",
"created": "Tue, 20 Sep 2016 14:38:32 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Panda",
"Priyadarshini",
""
],
[
"Venkataramani",
"Swagath",
""
],
[
"Sengupta",
"Abhronil",
""
],
[
"Raghunathan",
"Anand",
""
],
[
"Roy",
"Kaushik",
""
]
] | TITLE: Energy-Efficient Object Detection using Semantic Decomposition
ABSTRACT: Machine-learning algorithms offer immense possibilities in the development of
several cognitive applications. In fact, large scale machine-learning
classifiers now represent the state-of-the-art in a wide range of object
detection/classification problems. However, the network complexities of
large-scale classifiers present them as one of the most challenging and energy
intensive workloads across the computing spectrum. In this paper, we present a
new approach to optimize energy efficiency of object detection tasks using
semantic decomposition to build a hierarchical classification framework. We
observe that certain semantic information like color/texture are common across
various images in real-world datasets for object detection applications. We
exploit these common semantic features to distinguish the objects of interest
from the remaining inputs (non-objects of interest) in a dataset at a lower
computational effort. We propose a 2-stage hierarchical classification
framework, with increasing levels of complexity, wherein the first stage is
trained to recognize the broad representative semantic features relevant to the
object of interest. The first stage rejects the input instances that do not
have the representative features and passes only the relevant instances to the
second stage. Our methodology thus allows us to reject certain information at
lower complexity and utilize the full computational effort of a network only on
a smaller fraction of inputs to perform detection. We use color and texture as
distinctive traits to carry out several experiments for object detection. Our
experiments on the Caltech101/CIFAR10 dataset show that the proposed method
yields 1.93x/1.46x improvement in average energy, respectively, over the
traditional single classifier model.
| no_new_dataset | 0.949201 |
1604.01125 | Hang-Hyun Jo | Eun-Kyeong Kim and Hang-Hyun Jo | Measuring burstiness for finite event sequences | 7 pages, 3 figures | Phys. Rev. E 94, 032311 (2016) | 10.1103/PhysRevE.94.032311 | null | physics.soc-ph physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Characterizing inhomogeneous temporal patterns in natural and social
phenomena is important to understand underlying mechanisms behind such complex
systems, hence even to predict and control them. Temporal inhomogeneities in
event sequences have been described in terms of bursts that are rapidly
occurring events in short time periods alternating with long inactive periods.
The bursts can be quantified by a simple measure, called burstiness parameter,
which was introduced by Goh and Barab\'asi [EPL \textbf{81}, 48002 (2008)]. The
burstiness parameter has been widely used due to its simplicity, which however
turns out to be strongly affected by the finite number of events in the time
series. As the finite-size effects on burstiness parameter have been largely
ignored, we analytically investigate the finite-size effects of the burstiness
parameter. Then we suggest an alternative definition of burstiness that is free
from finite-size effects and yet simple. Using our alternative burstiness
measure, one can distinguish the finite-size effects from the intrinsic bursty
properties in the time series. We also demonstrate the advantages of our
burstiness measure by analyzing empirical datasets.
| [
{
"version": "v1",
"created": "Tue, 5 Apr 2016 03:42:19 GMT"
},
{
"version": "v2",
"created": "Mon, 29 Aug 2016 17:04:20 GMT"
},
{
"version": "v3",
"created": "Mon, 19 Sep 2016 02:10:48 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Kim",
"Eun-Kyeong",
""
],
[
"Jo",
"Hang-Hyun",
""
]
] | TITLE: Measuring burstiness for finite event sequences
ABSTRACT: Characterizing inhomogeneous temporal patterns in natural and social
phenomena is important to understand underlying mechanisms behind such complex
systems, hence even to predict and control them. Temporal inhomogeneities in
event sequences have been described in terms of bursts that are rapidly
occurring events in short time periods alternating with long inactive periods.
The bursts can be quantified by a simple measure, called burstiness parameter,
which was introduced by Goh and Barab\'asi [EPL \textbf{81}, 48002 (2008)]. The
burstiness parameter has been widely used due to its simplicity, which however
turns out to be strongly affected by the finite number of events in the time
series. As the finite-size effects on burstiness parameter have been largely
ignored, we analytically investigate the finite-size effects of the burstiness
parameter. Then we suggest an alternative definition of burstiness that is free
from finite-size effects and yet simple. Using our alternative burstiness
measure, one can distinguish the finite-size effects from the intrinsic bursty
properties in the time series. We also demonstrate the advantages of our
burstiness measure by analyzing empirical datasets.
| no_new_dataset | 0.946843 |
1604.07480 | Arsalan Mousavian | Arsalan Mousavian, Hamed Pirsiavash, Jana Kosecka | Joint Semantic Segmentation and Depth Estimation with Deep Convolutional
Networks | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-scale deep CNNs have been used successfully for problems mapping each
pixel to a label, such as depth estimation and semantic segmentation. It has
also been shown that such architectures are reusable and can be used for
multiple tasks. These networks are typically trained independently for each
task by varying the output layer(s) and training objective. In this work we
present a new model for simultaneous depth estimation and semantic segmentation
from a single RGB image. Our approach demonstrates the feasibility of training
parts of the model for each task and then fine tuning the full, combined model
on both tasks simultaneously using a single loss function. Furthermore we
couple the deep CNN with fully connected CRF, which captures the contextual
relationships and interactions between the semantic and depth cues improving
the accuracy of the final results. The proposed model is trained and evaluated
on NYUDepth V2 dataset outperforming the state of the art methods on semantic
segmentation and achieving comparable results on the task of depth estimation.
| [
{
"version": "v1",
"created": "Mon, 25 Apr 2016 23:58:00 GMT"
},
{
"version": "v2",
"created": "Thu, 8 Sep 2016 15:10:54 GMT"
},
{
"version": "v3",
"created": "Mon, 19 Sep 2016 21:57:28 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Mousavian",
"Arsalan",
""
],
[
"Pirsiavash",
"Hamed",
""
],
[
"Kosecka",
"Jana",
""
]
] | TITLE: Joint Semantic Segmentation and Depth Estimation with Deep Convolutional
Networks
ABSTRACT: Multi-scale deep CNNs have been used successfully for problems mapping each
pixel to a label, such as depth estimation and semantic segmentation. It has
also been shown that such architectures are reusable and can be used for
multiple tasks. These networks are typically trained independently for each
task by varying the output layer(s) and training objective. In this work we
present a new model for simultaneous depth estimation and semantic segmentation
from a single RGB image. Our approach demonstrates the feasibility of training
parts of the model for each task and then fine tuning the full, combined model
on both tasks simultaneously using a single loss function. Furthermore we
couple the deep CNN with fully connected CRF, which captures the contextual
relationships and interactions between the semantic and depth cues improving
the accuracy of the final results. The proposed model is trained and evaluated
on NYUDepth V2 dataset outperforming the state of the art methods on semantic
segmentation and achieving comparable results on the task of depth estimation.
| no_new_dataset | 0.948728 |
1606.05579 | Irina Higgins | Irina Higgins, Loic Matthey, Xavier Glorot, Arka Pal, Benigno Uria,
Charles Blundell, Shakir Mohamed, Alexander Lerchner | Early Visual Concept Learning with Unsupervised Deep Learning | null | null | null | null | stat.ML cs.LG q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automated discovery of early visual concepts from raw image data is a major
open challenge in AI research. Addressing this problem, we propose an
unsupervised approach for learning disentangled representations of the
underlying factors of variation. We draw inspiration from neuroscience, and
show how this can be achieved in an unsupervised generative model by applying
the same learning pressures as have been suggested to act in the ventral visual
stream in the brain. By enforcing redundancy reduction, encouraging statistical
independence, and exposure to data with transform continuities analogous to
those to which human infants are exposed, we obtain a variational autoencoder
(VAE) framework capable of learning disentangled factors. Our approach makes
few assumptions and works well across a wide variety of datasets. Furthermore,
our solution has useful emergent properties, such as zero-shot inference and an
intuitive understanding of "objectness".
| [
{
"version": "v1",
"created": "Fri, 17 Jun 2016 16:19:46 GMT"
},
{
"version": "v2",
"created": "Mon, 19 Sep 2016 19:50:49 GMT"
},
{
"version": "v3",
"created": "Tue, 20 Sep 2016 09:30:26 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Higgins",
"Irina",
""
],
[
"Matthey",
"Loic",
""
],
[
"Glorot",
"Xavier",
""
],
[
"Pal",
"Arka",
""
],
[
"Uria",
"Benigno",
""
],
[
"Blundell",
"Charles",
""
],
[
"Mohamed",
"Shakir",
""
],
[
"Lerchner",
"Alexander",
""
]
] | TITLE: Early Visual Concept Learning with Unsupervised Deep Learning
ABSTRACT: Automated discovery of early visual concepts from raw image data is a major
open challenge in AI research. Addressing this problem, we propose an
unsupervised approach for learning disentangled representations of the
underlying factors of variation. We draw inspiration from neuroscience, and
show how this can be achieved in an unsupervised generative model by applying
the same learning pressures as have been suggested to act in the ventral visual
stream in the brain. By enforcing redundancy reduction, encouraging statistical
independence, and exposure to data with transform continuities analogous to
those to which human infants are exposed, we obtain a variational autoencoder
(VAE) framework capable of learning disentangled factors. Our approach makes
few assumptions and works well across a wide variety of datasets. Furthermore,
our solution has useful emergent properties, such as zero-shot inference and an
intuitive understanding of "objectness".
| no_new_dataset | 0.947721 |
1608.00762 | Han Gong | Han Gong, Darren P. Cosker | Interactive Removal and Ground Truth for Difficult Shadow Scenes | Accepted by JOSA A | null | 10.1364/JOSAA.33.001798 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A user-centric method for fast, interactive, robust and high-quality shadow
removal is presented. Our algorithm can perform detection and removal in a
range of difficult cases: such as highly textured and colored shadows. To
perform detection an on-the-fly learning approach is adopted guided by two
rough user inputs for the pixels of the shadow and the lit area. After
detection, shadow removal is performed by registering the penumbra to a
normalized frame which allows us efficient estimation of non-uniform shadow
illumination changes, resulting in accurate and robust removal. Another major
contribution of this work is the first validated and multi-scene category
ground truth for shadow removal algorithms. This data set containing 186 images
eliminates inconsistencies between shadow and shadow-free images and provides a
range of different shadow types such as soft, textured, colored and broken
shadow. Using this data, the most thorough comparison of state-of-the-art
shadow removal methods to date is performed, showing our proposed new algorithm
to outperform the state-of-the-art across several measures and shadow category.
To complement our dataset, an online shadow removal benchmark website is also
presented to encourage future open comparisons in this challenging field of
research.
| [
{
"version": "v1",
"created": "Tue, 2 Aug 2016 10:51:07 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Gong",
"Han",
""
],
[
"Cosker",
"Darren P.",
""
]
] | TITLE: Interactive Removal and Ground Truth for Difficult Shadow Scenes
ABSTRACT: A user-centric method for fast, interactive, robust and high-quality shadow
removal is presented. Our algorithm can perform detection and removal in a
range of difficult cases: such as highly textured and colored shadows. To
perform detection an on-the-fly learning approach is adopted guided by two
rough user inputs for the pixels of the shadow and the lit area. After
detection, shadow removal is performed by registering the penumbra to a
normalized frame which allows us efficient estimation of non-uniform shadow
illumination changes, resulting in accurate and robust removal. Another major
contribution of this work is the first validated and multi-scene category
ground truth for shadow removal algorithms. This data set containing 186 images
eliminates inconsistencies between shadow and shadow-free images and provides a
range of different shadow types such as soft, textured, colored and broken
shadow. Using this data, the most thorough comparison of state-of-the-art
shadow removal methods to date is performed, showing our proposed new algorithm
to outperform the state-of-the-art across several measures and shadow category.
To complement our dataset, an online shadow removal benchmark website is also
presented to encourage future open comparisons in this challenging field of
research.
| new_dataset | 0.958538 |
1608.05571 | Martin Danelljan | Martin Danelljan, Gustav H\"ager, Fahad Shahbaz Khan, Michael Felsberg | Learning Spatially Regularized Correlation Filters for Visual Tracking | ICCV 2015 | International Conference on Computer Vision, (ICCV) 2015, pp.
4310-4318. IEEE (2015) | 10.1109/ICCV.2015.490 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robust and accurate visual tracking is one of the most challenging computer
vision problems. Due to the inherent lack of training data, a robust approach
for constructing a target appearance model is crucial. Recently,
discriminatively learned correlation filters (DCF) have been successfully
applied to address this problem for tracking. These methods utilize a periodic
assumption of the training samples to efficiently learn a classifier on all
patches in the target neighborhood. However, the periodic assumption also
introduces unwanted boundary effects, which severely degrade the quality of the
tracking model.
We propose Spatially Regularized Discriminative Correlation Filters (SRDCF)
for tracking. A spatial regularization component is introduced in the learning
to penalize correlation filter coefficients depending on their spatial
location. Our SRDCF formulation allows the correlation filters to be learned on
a significantly larger set of negative training samples, without corrupting the
positive samples. We further propose an optimization strategy, based on the
iterative Gauss-Seidel method, for efficient online learning of our SRDCF.
Experiments are performed on four benchmark datasets: OTB-2013, ALOV++,
OTB-2015, and VOT2014. Our approach achieves state-of-the-art results on all
four datasets. On OTB-2013 and OTB-2015, we obtain an absolute gain of 8.0% and
8.2% respectively, in mean overlap precision, compared to the best existing
trackers.
| [
{
"version": "v1",
"created": "Fri, 19 Aug 2016 11:11:49 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Danelljan",
"Martin",
""
],
[
"Häger",
"Gustav",
""
],
[
"Khan",
"Fahad Shahbaz",
""
],
[
"Felsberg",
"Michael",
""
]
] | TITLE: Learning Spatially Regularized Correlation Filters for Visual Tracking
ABSTRACT: Robust and accurate visual tracking is one of the most challenging computer
vision problems. Due to the inherent lack of training data, a robust approach
for constructing a target appearance model is crucial. Recently,
discriminatively learned correlation filters (DCF) have been successfully
applied to address this problem for tracking. These methods utilize a periodic
assumption of the training samples to efficiently learn a classifier on all
patches in the target neighborhood. However, the periodic assumption also
introduces unwanted boundary effects, which severely degrade the quality of the
tracking model.
We propose Spatially Regularized Discriminative Correlation Filters (SRDCF)
for tracking. A spatial regularization component is introduced in the learning
to penalize correlation filter coefficients depending on their spatial
location. Our SRDCF formulation allows the correlation filters to be learned on
a significantly larger set of negative training samples, without corrupting the
positive samples. We further propose an optimization strategy, based on the
iterative Gauss-Seidel method, for efficient online learning of our SRDCF.
Experiments are performed on four benchmark datasets: OTB-2013, ALOV++,
OTB-2015, and VOT2014. Our approach achieves state-of-the-art results on all
four datasets. On OTB-2013 and OTB-2015, we obtain an absolute gain of 8.0% and
8.2% respectively, in mean overlap precision, compared to the best existing
trackers.
| no_new_dataset | 0.947769 |
1609.05103 | Martin Theobald | Maximilian Dylla, Martin Theobald | Learning Tuple Probabilities | null | null | null | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning the parameters of complex probabilistic-relational models from
labeled training data is a standard technique in machine learning, which has
been intensively studied in the subfield of Statistical Relational Learning
(SRL), but---so far---this is still an under-investigated topic in the context
of Probabilistic Databases (PDBs). In this paper, we focus on learning the
probability values of base tuples in a PDB from labeled lineage formulas. The
resulting learning problem can be viewed as the inverse problem to confidence
computations in PDBs: given a set of labeled query answers, learn the
probability values of the base tuples, such that the marginal probabilities of
the query answers again yield in the assigned probability labels. We analyze
the learning problem from a theoretical perspective, cast it into an
optimization problem, and provide an algorithm based on stochastic gradient
descent. Finally, we conclude by an experimental evaluation on three real-world
and one synthetic dataset, thus comparing our approach to various techniques
from SRL, reasoning in information extraction, and optimization.
| [
{
"version": "v1",
"created": "Fri, 16 Sep 2016 15:16:25 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Sep 2016 06:36:11 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Dylla",
"Maximilian",
""
],
[
"Theobald",
"Martin",
""
]
] | TITLE: Learning Tuple Probabilities
ABSTRACT: Learning the parameters of complex probabilistic-relational models from
labeled training data is a standard technique in machine learning, which has
been intensively studied in the subfield of Statistical Relational Learning
(SRL), but---so far---this is still an under-investigated topic in the context
of Probabilistic Databases (PDBs). In this paper, we focus on learning the
probability values of base tuples in a PDB from labeled lineage formulas. The
resulting learning problem can be viewed as the inverse problem to confidence
computations in PDBs: given a set of labeled query answers, learn the
probability values of the base tuples, such that the marginal probabilities of
the query answers again yield in the assigned probability labels. We analyze
the learning problem from a theoretical perspective, cast it into an
optimization problem, and provide an algorithm based on stochastic gradient
descent. Finally, we conclude by an experimental evaluation on three real-world
and one synthetic dataset, thus comparing our approach to various techniques
from SRL, reasoning in information extraction, and optimization.
| no_new_dataset | 0.945197 |
1609.06018 | Junxuan Chen | Junxuan Chen, Baigui Sun, Hao Li, Hongtao Lu, Xian-Sheng Hua | Deep CTR Prediction in Display Advertising | This manuscript is the accepted version for ACM Multimedia Conference
2016 | null | null | null | cs.CV cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Click through rate (CTR) prediction of image ads is the core task of online
display advertising systems, and logistic regression (LR) has been frequently
applied as the prediction model. However, LR model lacks the ability of
extracting complex and intrinsic nonlinear features from handcrafted
high-dimensional image features, which limits its effectiveness. To solve this
issue, in this paper, we introduce a novel deep neural network (DNN) based
model that directly predicts the CTR of an image ad based on raw image pixels
and other basic features in one step. The DNN model employs convolution layers
to automatically extract representative visual features from images, and
nonlinear CTR features are then learned from visual features and other
contextual features by using fully-connected layers. Empirical evaluations on a
real world dataset with over 50 million records demonstrate the effectiveness
and efficiency of this method.
| [
{
"version": "v1",
"created": "Tue, 20 Sep 2016 04:50:03 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Chen",
"Junxuan",
""
],
[
"Sun",
"Baigui",
""
],
[
"Li",
"Hao",
""
],
[
"Lu",
"Hongtao",
""
],
[
"Hua",
"Xian-Sheng",
""
]
] | TITLE: Deep CTR Prediction in Display Advertising
ABSTRACT: Click through rate (CTR) prediction of image ads is the core task of online
display advertising systems, and logistic regression (LR) has been frequently
applied as the prediction model. However, LR model lacks the ability of
extracting complex and intrinsic nonlinear features from handcrafted
high-dimensional image features, which limits its effectiveness. To solve this
issue, in this paper, we introduce a novel deep neural network (DNN) based
model that directly predicts the CTR of an image ad based on raw image pixels
and other basic features in one step. The DNN model employs convolution layers
to automatically extract representative visual features from images, and
nonlinear CTR features are then learned from visual features and other
contextual features by using fully-connected layers. Empirical evaluations on a
real world dataset with over 50 million records demonstrate the effectiveness
and efficiency of this method.
| no_new_dataset | 0.951051 |
1609.06082 | Yitong Li | Yitong Li and Trevor Cohn and Timothy Baldwin | Learning Robust Representations of Text | 5 pages with 2 pages reference, 2 tables, 1 figure | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks have achieved remarkable results across many language
processing tasks, however these methods are highly sensitive to noise and
adversarial attacks. We present a regularization based method for limiting
network sensitivity to its inputs, inspired by ideas from computer vision, thus
learning models that are more robust. Empirical evaluation over a range of
sentiment datasets with a convolutional neural network shows that, compared to
a baseline model and the dropout method, our method achieves superior
performance over noisy inputs and out-of-domain data.
| [
{
"version": "v1",
"created": "Tue, 20 Sep 2016 10:23:47 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Li",
"Yitong",
""
],
[
"Cohn",
"Trevor",
""
],
[
"Baldwin",
"Timothy",
""
]
] | TITLE: Learning Robust Representations of Text
ABSTRACT: Deep neural networks have achieved remarkable results across many language
processing tasks, however these methods are highly sensitive to noise and
adversarial attacks. We present a regularization based method for limiting
network sensitivity to its inputs, inspired by ideas from computer vision, thus
learning models that are more robust. Empirical evaluation over a range of
sentiment datasets with a convolutional neural network shows that, compared to
a baseline model and the dropout method, our method achieves superior
performance over noisy inputs and out-of-domain data.
| no_new_dataset | 0.947332 |
1609.06118 | Martin Danelljan | Martin Danelljan, Gustav H\"ager, Fahad Shahbaz Khan, Michael Felsberg | Adaptive Decontamination of the Training Set: A Unified Formulation for
Discriminative Visual Tracking | CVPR 2016 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tracking-by-detection methods have demonstrated competitive performance in
recent years. In these approaches, the tracking model heavily relies on the
quality of the training set. Due to the limited amount of labeled training
data, additional samples need to be extracted and labeled by the tracker
itself. This often leads to the inclusion of corrupted training samples, due to
occlusions, misalignments and other perturbations. Existing
tracking-by-detection methods either ignore this problem, or employ a separate
component for managing the training set.
We propose a novel generic approach for alleviating the problem of corrupted
training samples in tracking-by-detection frameworks. Our approach dynamically
manages the training set by estimating the quality of the samples. Contrary to
existing approaches, we propose a unified formulation by minimizing a single
loss over both the target appearance model and the sample quality weights. The
joint formulation enables corrupted samples to be down-weighted while
increasing the impact of correct ones. Experiments are performed on three
benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color
with 128 videos. On the OTB-2015, our unified formulation significantly
improves the baseline, with a gain of 3.8% in mean overlap precision. Finally,
our method achieves state-of-the-art results on all three datasets. Code and
supplementary material are available at
http://www.cvl.isy.liu.se/research/objrec/visualtracking/decontrack/index.html .
| [
{
"version": "v1",
"created": "Tue, 20 Sep 2016 11:46:17 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Danelljan",
"Martin",
""
],
[
"Häger",
"Gustav",
""
],
[
"Khan",
"Fahad Shahbaz",
""
],
[
"Felsberg",
"Michael",
""
]
] | TITLE: Adaptive Decontamination of the Training Set: A Unified Formulation for
Discriminative Visual Tracking
ABSTRACT: Tracking-by-detection methods have demonstrated competitive performance in
recent years. In these approaches, the tracking model heavily relies on the
quality of the training set. Due to the limited amount of labeled training
data, additional samples need to be extracted and labeled by the tracker
itself. This often leads to the inclusion of corrupted training samples, due to
occlusions, misalignments and other perturbations. Existing
tracking-by-detection methods either ignore this problem, or employ a separate
component for managing the training set.
We propose a novel generic approach for alleviating the problem of corrupted
training samples in tracking-by-detection frameworks. Our approach dynamically
manages the training set by estimating the quality of the samples. Contrary to
existing approaches, we propose a unified formulation by minimizing a single
loss over both the target appearance model and the sample quality weights. The
joint formulation enables corrupted samples to be down-weighted while
increasing the impact of correct ones. Experiments are performed on three
benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color
with 128 videos. On the OTB-2015, our unified formulation significantly
improves the baseline, with a gain of 3.8% in mean overlap precision. Finally,
our method achieves state-of-the-art results on all three datasets. Code and
supplementary material are available at
http://www.cvl.isy.liu.se/research/objrec/visualtracking/decontrack/index.html .
| no_new_dataset | 0.944125 |
1609.06127 | Diana Al Jlailaty | Diana Jlailaty and Daniela Grigori and Khalid Belhajjame | A framework for mining process models from emails logs | 18 pages, 6 figures | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to its wide use in personal, but most importantly, professional contexts,
email represents a valuable source of information that can be harvested for
understanding, reengineering and repurposing undocumented business processes of
companies and institutions. Towards this aim, a few researchers investigated
the problem of extracting process oriented information from email logs in order
to take benefit of the many available process mining techniques and tools. In
this paper we go further in this direction, by proposing a new method for
mining process models from email logs that leverage unsupervised machine
learning techniques with little human involvement. Moreover, our method allows
to semi-automatically label emails with activity names, that can be used for
activity recognition in new incoming emails. A use case demonstrates the
usefulness of the proposed solution using a modest in size, yet real-world,
dataset containing emails that belong to two different process models.
| [
{
"version": "v1",
"created": "Tue, 20 Sep 2016 12:29:15 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Jlailaty",
"Diana",
""
],
[
"Grigori",
"Daniela",
""
],
[
"Belhajjame",
"Khalid",
""
]
] | TITLE: A framework for mining process models from emails logs
ABSTRACT: Due to its wide use in personal, but most importantly, professional contexts,
email represents a valuable source of information that can be harvested for
understanding, reengineering and repurposing undocumented business processes of
companies and institutions. Towards this aim, a few researchers investigated
the problem of extracting process oriented information from email logs in order
to take benefit of the many available process mining techniques and tools. In
this paper we go further in this direction, by proposing a new method for
mining process models from email logs that leverage unsupervised machine
learning techniques with little human involvement. Moreover, our method allows
to semi-automatically label emails with activity names, that can be used for
activity recognition in new incoming emails. A use case demonstrates the
usefulness of the proposed solution using a modest in size, yet real-world,
dataset containing emails that belong to two different process models.
| no_new_dataset | 0.939858 |
1609.06141 | Martin Danelljan | Martin Danelljan, Gustav H\"ager, Fahad Shahbaz Khan, Michael Felsberg | Discriminative Scale Space Tracking | To appear in TPAMI. This is the journal extension of the
VOT2014-winning DSST tracking method | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate scale estimation of a target is a challenging research problem in
visual object tracking. Most state-of-the-art methods employ an exhaustive
scale search to estimate the target size. The exhaustive search strategy is
computationally expensive and struggles when encountered with large scale
variations. This paper investigates the problem of accurate and robust scale
estimation in a tracking-by-detection framework. We propose a novel scale
adaptive tracking approach by learning separate discriminative correlation
filters for translation and scale estimation. The explicit scale filter is
learned online using the target appearance sampled at a set of different
scales. Contrary to standard approaches, our method directly learns the
appearance change induced by variations in the target scale. Additionally, we
investigate strategies to reduce the computational cost of our approach.
Extensive experiments are performed on the OTB and the VOT2014 datasets.
Compared to the standard exhaustive scale search, our approach achieves a gain
of 2.5% in average overlap precision on the OTB dataset. Additionally, our
method is computationally efficient, operating at a 50% higher frame rate
compared to the exhaustive scale search. Our method obtains the top rank in
performance by outperforming 19 state-of-the-art trackers on OTB and 37
state-of-the-art trackers on VOT2014.
| [
{
"version": "v1",
"created": "Tue, 20 Sep 2016 12:57:08 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Danelljan",
"Martin",
""
],
[
"Häger",
"Gustav",
""
],
[
"Khan",
"Fahad Shahbaz",
""
],
[
"Felsberg",
"Michael",
""
]
] | TITLE: Discriminative Scale Space Tracking
ABSTRACT: Accurate scale estimation of a target is a challenging research problem in
visual object tracking. Most state-of-the-art methods employ an exhaustive
scale search to estimate the target size. The exhaustive search strategy is
computationally expensive and struggles when encountered with large scale
variations. This paper investigates the problem of accurate and robust scale
estimation in a tracking-by-detection framework. We propose a novel scale
adaptive tracking approach by learning separate discriminative correlation
filters for translation and scale estimation. The explicit scale filter is
learned online using the target appearance sampled at a set of different
scales. Contrary to standard approaches, our method directly learns the
appearance change induced by variations in the target scale. Additionally, we
investigate strategies to reduce the computational cost of our approach.
Extensive experiments are performed on the OTB and the VOT2014 datasets.
Compared to the standard exhaustive scale search, our approach achieves a gain
of 2.5% in average overlap precision on the OTB dataset. Additionally, our
method is computationally efficient, operating at a 50% higher frame rate
compared to the exhaustive scale search. Our method obtains the top rank in
performance by outperforming 19 state-of-the-art trackers on OTB and 37
state-of-the-art trackers on VOT2014.
| no_new_dataset | 0.952042 |
1609.06192 | Florian Dubost | Florian Dubost, Loic Peter, Christian Rupprecht, Benjamin
Gutierrez-Becker, Nassir Navab | Hands-Free Segmentation of Medical Volumes via Binary Inputs | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel hands-free method to interactively segment 3D medical
volumes. In our scenario, a human user progressively segments an organ by
answering a series of questions of the form "Is this voxel inside the object to
segment?". At each iteration, the chosen question is defined as the one halving
a set of candidate segmentations given the answered questions. For a quick and
efficient exploration, these segmentations are sampled according to the
Metropolis-Hastings algorithm. Our sampling technique relies on a combination
of relaxed shape prior, learnt probability map and consistency with previous
answers. We demonstrate the potential of our strategy on a prostate
segmentation MRI dataset. Through the study of failure cases with synthetic
examples, we demonstrate the adaptation potential of our method. We also show
that our method outperforms two intuitive baselines: one based on random
questions, the other one being the thresholded probability map.
| [
{
"version": "v1",
"created": "Tue, 20 Sep 2016 14:18:40 GMT"
}
] | 2016-09-21T00:00:00 | [
[
"Dubost",
"Florian",
""
],
[
"Peter",
"Loic",
""
],
[
"Rupprecht",
"Christian",
""
],
[
"Gutierrez-Becker",
"Benjamin",
""
],
[
"Navab",
"Nassir",
""
]
] | TITLE: Hands-Free Segmentation of Medical Volumes via Binary Inputs
ABSTRACT: We propose a novel hands-free method to interactively segment 3D medical
volumes. In our scenario, a human user progressively segments an organ by
answering a series of questions of the form "Is this voxel inside the object to
segment?". At each iteration, the chosen question is defined as the one halving
a set of candidate segmentations given the answered questions. For a quick and
efficient exploration, these segmentations are sampled according to the
Metropolis-Hastings algorithm. Our sampling technique relies on a combination
of relaxed shape prior, learnt probability map and consistency with previous
answers. We demonstrate the potential of our strategy on a prostate
segmentation MRI dataset. Through the study of failure cases with synthetic
examples, we demonstrate the adaptation potential of our method. We also show
that our method outperforms two intuitive baselines: one based on random
questions, the other one being the thresholded probability map.
| no_new_dataset | 0.948917 |
1512.00296 | Vinay Jayaram | Vinay Jayaram, Morteza Alamgir, Yasemin Altun, Bernhard Sch\"olkopf,
Moritz Grosse-Wentrup | Transfer Learning in Brain-Computer Interfaces | To be published in IEEE Computational Intelligence Magazine, special
BCI issue on January 15th online | null | 10.1109/MCI.2015.2501545 | null | cs.HC q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The performance of brain-computer interfaces (BCIs) improves with the amount
of available training data, the statistical distribution of this data, however,
varies across subjects as well as across sessions within individual subjects,
limiting the transferability of training data or trained models between them.
In this article, we review current transfer learning techniques in BCIs that
exploit shared structure between training data of multiple subjects and/or
sessions to increase performance. We then present a framework for transfer
learning in the context of BCIs that can be applied to any arbitrary feature
space, as well as a novel regression estimation method that is specifically
designed for the structure of a system based on the electroencephalogram (EEG).
We demonstrate the utility of our framework and method on subject-to-subject
transfer in a motor-imagery paradigm as well as on session-to-session transfer
in one patient diagnosed with amyotrophic lateral sclerosis (ALS), showing that
it is able to outperform other comparable methods on an identical dataset.
| [
{
"version": "v1",
"created": "Tue, 1 Dec 2015 15:33:24 GMT"
}
] | 2016-09-20T00:00:00 | [
[
"Jayaram",
"Vinay",
""
],
[
"Alamgir",
"Morteza",
""
],
[
"Altun",
"Yasemin",
""
],
[
"Schölkopf",
"Bernhard",
""
],
[
"Grosse-Wentrup",
"Moritz",
""
]
] | TITLE: Transfer Learning in Brain-Computer Interfaces
ABSTRACT: The performance of brain-computer interfaces (BCIs) improves with the amount
of available training data, the statistical distribution of this data, however,
varies across subjects as well as across sessions within individual subjects,
limiting the transferability of training data or trained models between them.
In this article, we review current transfer learning techniques in BCIs that
exploit shared structure between training data of multiple subjects and/or
sessions to increase performance. We then present a framework for transfer
learning in the context of BCIs that can be applied to any arbitrary feature
space, as well as a novel regression estimation method that is specifically
designed for the structure of a system based on the electroencephalogram (EEG).
We demonstrate the utility of our framework and method on subject-to-subject
transfer in a motor-imagery paradigm as well as on session-to-session transfer
in one patient diagnosed with amyotrophic lateral sclerosis (ALS), showing that
it is able to outperform other comparable methods on an identical dataset.
| no_new_dataset | 0.949482 |
1603.06679 | Wenya Wang | Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier and Xiaokui Xiao | Recursive Neural Conditional Random Fields for Aspect-based Sentiment
Analysis | null | null | null | null | cs.CL cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In aspect-based sentiment analysis, extracting aspect terms along with the
opinions being expressed from user-generated content is one of the most
important subtasks. Previous studies have shown that exploiting connections
between aspect and opinion terms is promising for this task. In this paper, we
propose a novel joint model that integrates recursive neural networks and
conditional random fields into a unified framework for explicit aspect and
opinion terms co-extraction. The proposed model learns high-level
discriminative features and double propagate information between aspect and
opinion terms, simultaneously. Moreover, it is flexible to incorporate
hand-crafted features into the proposed model to further boost its information
extraction performance. Experimental results on the SemEval Challenge 2014
dataset show the superiority of our proposed model over several baseline
methods as well as the winning systems of the challenge.
| [
{
"version": "v1",
"created": "Tue, 22 Mar 2016 05:59:00 GMT"
},
{
"version": "v2",
"created": "Wed, 8 Jun 2016 06:24:06 GMT"
},
{
"version": "v3",
"created": "Mon, 19 Sep 2016 14:00:43 GMT"
}
] | 2016-09-20T00:00:00 | [
[
"Wang",
"Wenya",
""
],
[
"Pan",
"Sinno Jialin",
""
],
[
"Dahlmeier",
"Daniel",
""
],
[
"Xiao",
"Xiaokui",
""
]
] | TITLE: Recursive Neural Conditional Random Fields for Aspect-based Sentiment
Analysis
ABSTRACT: In aspect-based sentiment analysis, extracting aspect terms along with the
opinions being expressed from user-generated content is one of the most
important subtasks. Previous studies have shown that exploiting connections
between aspect and opinion terms is promising for this task. In this paper, we
propose a novel joint model that integrates recursive neural networks and
conditional random fields into a unified framework for explicit aspect and
opinion terms co-extraction. The proposed model learns high-level
discriminative features and double propagate information between aspect and
opinion terms, simultaneously. Moreover, it is flexible to incorporate
hand-crafted features into the proposed model to further boost its information
extraction performance. Experimental results on the SemEval Challenge 2014
dataset show the superiority of our proposed model over several baseline
methods as well as the winning systems of the challenge.
| no_new_dataset | 0.949342 |
1603.08981 | Shuang Li | Shuang Li, Yao Xie, Mehrdad Farajtabar, Apurv Verma, and Le Song | Detecting weak changes in dynamic events over networks | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large volume of networked streaming event data are becoming increasingly
available in a wide variety of applications, such as social network analysis,
Internet traffic monitoring and healthcare analytics. Streaming event data are
discrete observation occurred in continuous time, and the precise time interval
between two events carries a great deal of information about the dynamics of
the underlying systems. How to promptly detect changes in these dynamic systems
using these streaming event data? In this paper, we propose a novel
change-point detection framework for multi-dimensional event data over
networks. We cast the problem into sequential hypothesis test, and derive the
likelihood ratios for point processes, which are computed efficiently via an
EM-like algorithm that is parameter-free and can be computed in a distributed
fashion. We derive a highly accurate theoretical characterization of the
false-alarm-rate, and show that it can achieve weak signal detection by
aggregating local statistics over time and networks. Finally, we demonstrate
the good performance of our algorithm on numerical examples and real-world
datasets from twitter and Memetracker.
| [
{
"version": "v1",
"created": "Tue, 29 Mar 2016 21:54:56 GMT"
},
{
"version": "v2",
"created": "Fri, 16 Sep 2016 20:09:56 GMT"
}
] | 2016-09-20T00:00:00 | [
[
"Li",
"Shuang",
""
],
[
"Xie",
"Yao",
""
],
[
"Farajtabar",
"Mehrdad",
""
],
[
"Verma",
"Apurv",
""
],
[
"Song",
"Le",
""
]
] | TITLE: Detecting weak changes in dynamic events over networks
ABSTRACT: Large volume of networked streaming event data are becoming increasingly
available in a wide variety of applications, such as social network analysis,
Internet traffic monitoring and healthcare analytics. Streaming event data are
discrete observation occurred in continuous time, and the precise time interval
between two events carries a great deal of information about the dynamics of
the underlying systems. How to promptly detect changes in these dynamic systems
using these streaming event data? In this paper, we propose a novel
change-point detection framework for multi-dimensional event data over
networks. We cast the problem into sequential hypothesis test, and derive the
likelihood ratios for point processes, which are computed efficiently via an
EM-like algorithm that is parameter-free and can be computed in a distributed
fashion. We derive a highly accurate theoretical characterization of the
false-alarm-rate, and show that it can achieve weak signal detection by
aggregating local statistics over time and networks. Finally, we demonstrate
the good performance of our algorithm on numerical examples and real-world
datasets from twitter and Memetracker.
| no_new_dataset | 0.946151 |
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