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1705.10447 | Jimmy Ren | Jimmy Ren, Zhiyang Yu, Jianbo Liu, Rui Zhang, Wenxiu Sun, Jiahao Pang,
Xiaohao Chen, Qiong Yan | Robust Tracking Using Region Proposal Networks | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in visual tracking showed that deep Convolutional Neural
Networks (CNN) trained for image classification can be strong feature
extractors for discriminative trackers. However, due to the drastic difference
between image classification and tracking, extra treatments such as model
ensemble and feature engineering must be carried out to bridge the two domains.
Such procedures are either time consuming or hard to generalize well across
datasets. In this paper we discovered that the internal structure of Region
Proposal Network (RPN)'s top layer feature can be utilized for robust visual
tracking. We showed that such property has to be unleashed by a novel loss
function which simultaneously considers classification accuracy and bounding
box quality. Without ensemble and any extra treatment on feature maps, our
proposed method achieved state-of-the-art results on several large scale
benchmarks including OTB50, OTB100 and VOT2016. We will make our code publicly
available.
| [
{
"version": "v1",
"created": "Tue, 30 May 2017 03:32:07 GMT"
}
] | 2017-05-31T00:00:00 | [
[
"Ren",
"Jimmy",
""
],
[
"Yu",
"Zhiyang",
""
],
[
"Liu",
"Jianbo",
""
],
[
"Zhang",
"Rui",
""
],
[
"Sun",
"Wenxiu",
""
],
[
"Pang",
"Jiahao",
""
],
[
"Chen",
"Xiaohao",
""
],
[
"Yan",
"Qiong",
""
]
] | TITLE: Robust Tracking Using Region Proposal Networks
ABSTRACT: Recent advances in visual tracking showed that deep Convolutional Neural
Networks (CNN) trained for image classification can be strong feature
extractors for discriminative trackers. However, due to the drastic difference
between image classification and tracking, extra treatments such as model
ensemble and feature engineering must be carried out to bridge the two domains.
Such procedures are either time consuming or hard to generalize well across
datasets. In this paper we discovered that the internal structure of Region
Proposal Network (RPN)'s top layer feature can be utilized for robust visual
tracking. We showed that such property has to be unleashed by a novel loss
function which simultaneously considers classification accuracy and bounding
box quality. Without ensemble and any extra treatment on feature maps, our
proposed method achieved state-of-the-art results on several large scale
benchmarks including OTB50, OTB100 and VOT2016. We will make our code publicly
available.
| no_new_dataset | 0.946448 |
1705.10586 | Zhenzhou Wu | Zhenzhou Wu and Xin Zheng and Daniel Dahlmeier | Character-Based Text Classification using Top Down Semantic Model for
Sentence Representation | null | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Despite the success of deep learning on many fronts especially image and
speech, its application in text classification often is still not as good as a
simple linear SVM on n-gram TF-IDF representation especially for smaller
datasets. Deep learning tends to emphasize on sentence level semantics when
learning a representation with models like recurrent neural network or
recursive neural network, however from the success of TF-IDF representation, it
seems a bag-of-words type of representation has its strength. Taking advantage
of both representions, we present a model known as TDSM (Top Down Semantic
Model) for extracting a sentence representation that considers both the
word-level semantics by linearly combining the words with attention weights and
the sentence-level semantics with BiLSTM and use it on text classification. We
apply the model on characters and our results show that our model is better
than all the other character-based and word-based convolutional neural network
models by \cite{zhang15} across seven different datasets with only 1\% of their
parameters. We also demonstrate that this model beats traditional linear models
on TF-IDF vectors on small and polished datasets like news article in which
typically deep learning models surrender.
| [
{
"version": "v1",
"created": "Mon, 29 May 2017 15:53:00 GMT"
}
] | 2017-05-31T00:00:00 | [
[
"Wu",
"Zhenzhou",
""
],
[
"Zheng",
"Xin",
""
],
[
"Dahlmeier",
"Daniel",
""
]
] | TITLE: Character-Based Text Classification using Top Down Semantic Model for
Sentence Representation
ABSTRACT: Despite the success of deep learning on many fronts especially image and
speech, its application in text classification often is still not as good as a
simple linear SVM on n-gram TF-IDF representation especially for smaller
datasets. Deep learning tends to emphasize on sentence level semantics when
learning a representation with models like recurrent neural network or
recursive neural network, however from the success of TF-IDF representation, it
seems a bag-of-words type of representation has its strength. Taking advantage
of both representions, we present a model known as TDSM (Top Down Semantic
Model) for extracting a sentence representation that considers both the
word-level semantics by linearly combining the words with attention weights and
the sentence-level semantics with BiLSTM and use it on text classification. We
apply the model on characters and our results show that our model is better
than all the other character-based and word-based convolutional neural network
models by \cite{zhang15} across seven different datasets with only 1\% of their
parameters. We also demonstrate that this model beats traditional linear models
on TF-IDF vectors on small and polished datasets like news article in which
typically deep learning models surrender.
| no_new_dataset | 0.954009 |
1705.10659 | Xiatian Zhu | Jingya Wang, Xiatian Zhu, Shaogang Gong | Discovering Visual Concept Structure with Sparse and Incomplete Tags | Artificial Intelligence journal 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Discovering automatically the semantic structure of tagged visual data (e.g.
web videos and images) is important for visual data analysis and
interpretation, enabling the machine intelligence for effectively processing
the fast-growing amount of multi-media data. However, this is non-trivial due
to the need for jointly learning underlying correlations between heterogeneous
visual and tag data. The task is made more challenging by inherently sparse and
incomplete tags. In this work, we develop a method for modelling the inherent
visual data concept structures based on a novel Hierarchical-Multi-Label Random
Forest model capable of correlating structured visual and tag information so as
to more accurately interpret the visual semantics, e.g. disclosing meaningful
visual groups with similar high-level concepts, and recovering missing tags for
individual visual data samples. Specifically, our model exploits hierarchically
structured tags of different semantic abstractness and multiple tag statistical
correlations in addition to modelling visual and tag interactions. As a result,
our model is able to discover more accurate semantic correlation between
textual tags and visual features, and finally providing favourable visual
semantics interpretation even with highly sparse and incomplete tags. We
demonstrate the advantages of our proposed approach in two fundamental
applications, visual data clustering and missing tag completion, on
benchmarking video (i.e. TRECVID MED 2011) and image (i.e. NUS-WIDE) datasets.
| [
{
"version": "v1",
"created": "Tue, 30 May 2017 14:12:43 GMT"
}
] | 2017-05-31T00:00:00 | [
[
"Wang",
"Jingya",
""
],
[
"Zhu",
"Xiatian",
""
],
[
"Gong",
"Shaogang",
""
]
] | TITLE: Discovering Visual Concept Structure with Sparse and Incomplete Tags
ABSTRACT: Discovering automatically the semantic structure of tagged visual data (e.g.
web videos and images) is important for visual data analysis and
interpretation, enabling the machine intelligence for effectively processing
the fast-growing amount of multi-media data. However, this is non-trivial due
to the need for jointly learning underlying correlations between heterogeneous
visual and tag data. The task is made more challenging by inherently sparse and
incomplete tags. In this work, we develop a method for modelling the inherent
visual data concept structures based on a novel Hierarchical-Multi-Label Random
Forest model capable of correlating structured visual and tag information so as
to more accurately interpret the visual semantics, e.g. disclosing meaningful
visual groups with similar high-level concepts, and recovering missing tags for
individual visual data samples. Specifically, our model exploits hierarchically
structured tags of different semantic abstractness and multiple tag statistical
correlations in addition to modelling visual and tag interactions. As a result,
our model is able to discover more accurate semantic correlation between
textual tags and visual features, and finally providing favourable visual
semantics interpretation even with highly sparse and incomplete tags. We
demonstrate the advantages of our proposed approach in two fundamental
applications, visual data clustering and missing tag completion, on
benchmarking video (i.e. TRECVID MED 2011) and image (i.e. NUS-WIDE) datasets.
| no_new_dataset | 0.948298 |
1705.10698 | Mark Marsden | Mark Marsden, Kevin McGuinness, Suzanne Little, Noel E. O'Connor | ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting,
Violent Behaviour Detection and Crowd Density Level Classification | 7 Pages, AVSS 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we propose ResnetCrowd, a deep residual architecture for
simultaneous crowd counting, violent behaviour detection and crowd density
level classification. To train and evaluate the proposed multi-objective
technique, a new 100 image dataset referred to as Multi Task Crowd is
constructed. This new dataset is the first computer vision dataset fully
annotated for crowd counting, violent behaviour detection and density level
classification. Our experiments show that a multi-task approach boosts
individual task performance for all tasks and most notably for violent
behaviour detection which receives a 9\% boost in ROC curve AUC (Area under the
curve). The trained ResnetCrowd model is also evaluated on several additional
benchmarks highlighting the superior generalisation of crowd analysis models
trained for multiple objectives.
| [
{
"version": "v1",
"created": "Tue, 30 May 2017 15:18:41 GMT"
}
] | 2017-05-31T00:00:00 | [
[
"Marsden",
"Mark",
""
],
[
"McGuinness",
"Kevin",
""
],
[
"Little",
"Suzanne",
""
],
[
"O'Connor",
"Noel E.",
""
]
] | TITLE: ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting,
Violent Behaviour Detection and Crowd Density Level Classification
ABSTRACT: In this paper we propose ResnetCrowd, a deep residual architecture for
simultaneous crowd counting, violent behaviour detection and crowd density
level classification. To train and evaluate the proposed multi-objective
technique, a new 100 image dataset referred to as Multi Task Crowd is
constructed. This new dataset is the first computer vision dataset fully
annotated for crowd counting, violent behaviour detection and density level
classification. Our experiments show that a multi-task approach boosts
individual task performance for all tasks and most notably for violent
behaviour detection which receives a 9\% boost in ROC curve AUC (Area under the
curve). The trained ResnetCrowd model is also evaluated on several additional
benchmarks highlighting the superior generalisation of crowd analysis models
trained for multiple objectives.
| new_dataset | 0.955444 |
1705.10716 | Ali Taalimi | Ali Taalimi, Liu Liu and Hairong Qi | Addressing Ambiguity in Multi-target Tracking by Hierarchical Strategy | 5 pages, Accepted in International Conference of Image Processing,
2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a novel hierarchical approach for the simultaneous
tracking of multiple targets in a video. We use a network flow approach to link
detections in low-level and tracklets in high-level. At each step of the
hierarchy, the confidence of candidates is measured by using a new scoring
system, ConfRank, that considers the quality and the quantity of its
neighborhood. The output of the first stage is a collection of safe tracklets
and unlinked high-confidence detections. For each individual detection, we
determine if it belongs to an existing or is a new tracklet. We show the effect
of our framework to recover missed detections and reduce switch identity. The
proposed tracker is referred to as TVOD for multi-target tracking using the
visual tracker and generic object detector. We achieve competitive results with
lower identity switches on several datasets comparing to state-of-the-art.
| [
{
"version": "v1",
"created": "Tue, 30 May 2017 16:11:34 GMT"
}
] | 2017-05-31T00:00:00 | [
[
"Taalimi",
"Ali",
""
],
[
"Liu",
"Liu",
""
],
[
"Qi",
"Hairong",
""
]
] | TITLE: Addressing Ambiguity in Multi-target Tracking by Hierarchical Strategy
ABSTRACT: This paper presents a novel hierarchical approach for the simultaneous
tracking of multiple targets in a video. We use a network flow approach to link
detections in low-level and tracklets in high-level. At each step of the
hierarchy, the confidence of candidates is measured by using a new scoring
system, ConfRank, that considers the quality and the quantity of its
neighborhood. The output of the first stage is a collection of safe tracklets
and unlinked high-confidence detections. For each individual detection, we
determine if it belongs to an existing or is a new tracklet. We show the effect
of our framework to recover missed detections and reduce switch identity. The
proposed tracker is referred to as TVOD for multi-target tracking using the
visual tracker and generic object detector. We achieve competitive results with
lower identity switches on several datasets comparing to state-of-the-art.
| no_new_dataset | 0.94545 |
1705.10742 | Martin Jaggi | Tina Fang, Martin Jaggi, Katerina Argyraki | Generating Steganographic Text with LSTMs | ACL 2017 Student Research Workshop | null | null | null | cs.AI cs.CR cs.MM | http://creativecommons.org/licenses/by/4.0/ | Motivated by concerns for user privacy, we design a steganographic system
("stegosystem") that enables two users to exchange encrypted messages without
an adversary detecting that such an exchange is taking place. We propose a new
linguistic stegosystem based on a Long Short-Term Memory (LSTM) neural network.
We demonstrate our approach on the Twitter and Enron email datasets and show
that it yields high-quality steganographic text while significantly improving
capacity (encrypted bits per word) relative to the state-of-the-art.
| [
{
"version": "v1",
"created": "Tue, 30 May 2017 16:52:48 GMT"
}
] | 2017-05-31T00:00:00 | [
[
"Fang",
"Tina",
""
],
[
"Jaggi",
"Martin",
""
],
[
"Argyraki",
"Katerina",
""
]
] | TITLE: Generating Steganographic Text with LSTMs
ABSTRACT: Motivated by concerns for user privacy, we design a steganographic system
("stegosystem") that enables two users to exchange encrypted messages without
an adversary detecting that such an exchange is taking place. We propose a new
linguistic stegosystem based on a Long Short-Term Memory (LSTM) neural network.
We demonstrate our approach on the Twitter and Enron email datasets and show
that it yields high-quality steganographic text while significantly improving
capacity (encrypted bits per word) relative to the state-of-the-art.
| no_new_dataset | 0.951953 |
1705.10744 | Ondrej Bajgar | Rudolf Kadlec, Ondrej Bajgar and Jan Kleindienst | Knowledge Base Completion: Baselines Strike Back | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many papers have been published on the knowledge base completion task in the
past few years. Most of these introduce novel architectures for relation
learning that are evaluated on standard datasets such as FB15k and WN18. This
paper shows that the accuracy of almost all models published on the FB15k can
be outperformed by an appropriately tuned baseline - our reimplementation of
the DistMult model. Our findings cast doubt on the claim that the performance
improvements of recent models are due to architectural changes as opposed to
hyper-parameter tuning or different training objectives. This should prompt
future research to re-consider how the performance of models is evaluated and
reported.
| [
{
"version": "v1",
"created": "Tue, 30 May 2017 16:54:19 GMT"
}
] | 2017-05-31T00:00:00 | [
[
"Kadlec",
"Rudolf",
""
],
[
"Bajgar",
"Ondrej",
""
],
[
"Kleindienst",
"Jan",
""
]
] | TITLE: Knowledge Base Completion: Baselines Strike Back
ABSTRACT: Many papers have been published on the knowledge base completion task in the
past few years. Most of these introduce novel architectures for relation
learning that are evaluated on standard datasets such as FB15k and WN18. This
paper shows that the accuracy of almost all models published on the FB15k can
be outperformed by an appropriately tuned baseline - our reimplementation of
the DistMult model. Our findings cast doubt on the claim that the performance
improvements of recent models are due to architectural changes as opposed to
hyper-parameter tuning or different training objectives. This should prompt
future research to re-consider how the performance of models is evaluated and
reported.
| no_new_dataset | 0.944689 |
1705.10750 | Junier Oliva | Junier B. Oliva, Kumar Avinava Dubey, Barnabas Poczos, Eric Xing, Jeff
Schneider | Recurrent Estimation of Distributions | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents the recurrent estimation of distributions (RED) for
modeling real-valued data in a semiparametric fashion. RED models make two
novel uses of recurrent neural networks (RNNs) for density estimation of
general real-valued data. First, RNNs are used to transform input covariates
into a latent space to better capture conditional dependencies in inputs.
After, an RNN is used to compute the conditional distributions of the latent
covariates. The resulting model is efficient to train, compute, and sample
from, whilst producing normalized pdfs. The effectiveness of RED is shown via
several real-world data experiments. Our results show that RED models achieve a
lower held-out negative log-likelihood than other neural network approaches
across multiple dataset sizes and dimensionalities. Further context of the
efficacy of RED is provided by considering anomaly detection tasks, where we
also observe better performance over alternative models.
| [
{
"version": "v1",
"created": "Tue, 30 May 2017 17:00:59 GMT"
}
] | 2017-05-31T00:00:00 | [
[
"Oliva",
"Junier B.",
""
],
[
"Dubey",
"Kumar Avinava",
""
],
[
"Poczos",
"Barnabas",
""
],
[
"Xing",
"Eric",
""
],
[
"Schneider",
"Jeff",
""
]
] | TITLE: Recurrent Estimation of Distributions
ABSTRACT: This paper presents the recurrent estimation of distributions (RED) for
modeling real-valued data in a semiparametric fashion. RED models make two
novel uses of recurrent neural networks (RNNs) for density estimation of
general real-valued data. First, RNNs are used to transform input covariates
into a latent space to better capture conditional dependencies in inputs.
After, an RNN is used to compute the conditional distributions of the latent
covariates. The resulting model is efficient to train, compute, and sample
from, whilst producing normalized pdfs. The effectiveness of RED is shown via
several real-world data experiments. Our results show that RED models achieve a
lower held-out negative log-likelihood than other neural network approaches
across multiple dataset sizes and dimensionalities. Further context of the
efficacy of RED is provided by considering anomaly detection tasks, where we
also observe better performance over alternative models.
| no_new_dataset | 0.948775 |
1705.10754 | Francisco Rangel | Francisco Rangel and Marc Franco-Salvador and Paolo Rosso | A Low Dimensionality Representation for Language Variety Identification | null | CICLing - Computational Linguistics and Intelligent Text
Processing, 2016 | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Language variety identification aims at labelling texts in a native language
(e.g. Spanish, Portuguese, English) with its specific variation (e.g.
Argentina, Chile, Mexico, Peru, Spain; Brazil, Portugal; UK, US). In this work
we propose a low dimensionality representation (LDR) to address this task with
five different varieties of Spanish: Argentina, Chile, Mexico, Peru and Spain.
We compare our LDR method with common state-of-the-art representations and show
an increase in accuracy of ~35%. Furthermore, we compare LDR with two reference
distributed representation models. Experimental results show competitive
performance while dramatically reducing the dimensionality --and increasing the
big data suitability-- to only 6 features per variety. Additionally, we analyse
the behaviour of the employed machine learning algorithms and the most
discriminating features. Finally, we employ an alternative dataset to test the
robustness of our low dimensionality representation with another set of similar
languages.
| [
{
"version": "v1",
"created": "Tue, 30 May 2017 17:07:45 GMT"
}
] | 2017-05-31T00:00:00 | [
[
"Rangel",
"Francisco",
""
],
[
"Franco-Salvador",
"Marc",
""
],
[
"Rosso",
"Paolo",
""
]
] | TITLE: A Low Dimensionality Representation for Language Variety Identification
ABSTRACT: Language variety identification aims at labelling texts in a native language
(e.g. Spanish, Portuguese, English) with its specific variation (e.g.
Argentina, Chile, Mexico, Peru, Spain; Brazil, Portugal; UK, US). In this work
we propose a low dimensionality representation (LDR) to address this task with
five different varieties of Spanish: Argentina, Chile, Mexico, Peru and Spain.
We compare our LDR method with common state-of-the-art representations and show
an increase in accuracy of ~35%. Furthermore, we compare LDR with two reference
distributed representation models. Experimental results show competitive
performance while dramatically reducing the dimensionality --and increasing the
big data suitability-- to only 6 features per variety. Additionally, we analyse
the behaviour of the employed machine learning algorithms and the most
discriminating features. Finally, we employ an alternative dataset to test the
robustness of our low dimensionality representation with another set of similar
languages.
| new_dataset | 0.514309 |
1604.06518 | Vu Nguyen | Trung Le and Tu Dinh Nguyen and Vu Nguyen and Dinh Phung | Approximation Vector Machines for Large-scale Online Learning | 54 pages | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the most challenging problems in kernel online learning is to bound
the model size and to promote the model sparsity. Sparse models not only
improve computation and memory usage, but also enhance the generalization
capacity, a principle that concurs with the law of parsimony. However,
inappropriate sparsity modeling may also significantly degrade the performance.
In this paper, we propose Approximation Vector Machine (AVM), a model that can
simultaneously encourage the sparsity and safeguard its risk in compromising
the performance. When an incoming instance arrives, we approximate this
instance by one of its neighbors whose distance to it is less than a predefined
threshold. Our key intuition is that since the newly seen instance is expressed
by its nearby neighbor the optimal performance can be analytically formulated
and maintained. We develop theoretical foundations to support this intuition
and further establish an analysis to characterize the gap between the
approximation and optimal solutions. This gap crucially depends on the
frequency of approximation and the predefined threshold. We perform the
convergence analysis for a wide spectrum of loss functions including Hinge,
smooth Hinge, and Logistic for classification task, and $l_1$, $l_2$, and
$\epsilon$-insensitive for regression task. We conducted extensive experiments
for classification task in batch and online modes, and regression task in
online mode over several benchmark datasets. The results show that our proposed
AVM achieved a comparable predictive performance with current state-of-the-art
methods while simultaneously achieving significant computational speed-up due
to the ability of the proposed AVM in maintaining the model size.
| [
{
"version": "v1",
"created": "Fri, 22 Apr 2016 01:57:01 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Apr 2016 01:16:21 GMT"
},
{
"version": "v3",
"created": "Wed, 5 Apr 2017 01:43:29 GMT"
},
{
"version": "v4",
"created": "Sun, 28 May 2017 01:26:48 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Le",
"Trung",
""
],
[
"Nguyen",
"Tu Dinh",
""
],
[
"Nguyen",
"Vu",
""
],
[
"Phung",
"Dinh",
""
]
] | TITLE: Approximation Vector Machines for Large-scale Online Learning
ABSTRACT: One of the most challenging problems in kernel online learning is to bound
the model size and to promote the model sparsity. Sparse models not only
improve computation and memory usage, but also enhance the generalization
capacity, a principle that concurs with the law of parsimony. However,
inappropriate sparsity modeling may also significantly degrade the performance.
In this paper, we propose Approximation Vector Machine (AVM), a model that can
simultaneously encourage the sparsity and safeguard its risk in compromising
the performance. When an incoming instance arrives, we approximate this
instance by one of its neighbors whose distance to it is less than a predefined
threshold. Our key intuition is that since the newly seen instance is expressed
by its nearby neighbor the optimal performance can be analytically formulated
and maintained. We develop theoretical foundations to support this intuition
and further establish an analysis to characterize the gap between the
approximation and optimal solutions. This gap crucially depends on the
frequency of approximation and the predefined threshold. We perform the
convergence analysis for a wide spectrum of loss functions including Hinge,
smooth Hinge, and Logistic for classification task, and $l_1$, $l_2$, and
$\epsilon$-insensitive for regression task. We conducted extensive experiments
for classification task in batch and online modes, and regression task in
online mode over several benchmark datasets. The results show that our proposed
AVM achieved a comparable predictive performance with current state-of-the-art
methods while simultaneously achieving significant computational speed-up due
to the ability of the proposed AVM in maintaining the model size.
| no_new_dataset | 0.946151 |
1606.06159 | Jie Sun | Yazhen Jiang, Joseph Skufca, Jie Sun | BiFold visualization of bipartite datasets | 18 pages, 6 figures | EPJ Data Sci. (2017) 6: 2 | 10.1140/epjds/s13688-017-0098-4 | null | cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The emerging domain of data-enabled science necessitates development of
algorithms and tools for knowledge discovery. Human interaction with data
through well-constructed graphical representation can take special advantage of
our visual ability to identify patterns. We develop a data visualization
framework, called BiFold, for exploratory analysis of bipartite datasets that
describe binary relationships between groups of objects. Typical data examples
would include voting records, organizational memberships, and pairwise
associations, or other binary datasets. BiFold provides a low dimensional
embedding of data that represents similarity by visual nearness, analogous to
Multidimensional Scaling (MDS). The unique and new feature of BiFold is its
ability to simultaneously capture both within-group and between-group
relationships among objects, enhancing knowledge discovery. We benchmark BiFold
using the {\it Southern Women Dataset}, where social groups are now visually
evident. We construct BiFold plots for two US voting datasets: For the
presidential election outcomes since 1976, BiFold illustrates the evolving
geopolitical structures that underlie these election results. For Senate
congressional voting, BiFold identifies a partisan coordinate, separating
senators into two parties while simultaneously visualizing a
bipartisan-coalition coordinate which captures the ultimate fate of the bills
(pass/fail). Finally, we consider a global cuisine dataset of the association
between recipes and food ingredients. BiFold allows us to visually compare and
contrast cuisines while also allowing identification of signature ingredients
of individual cuisines.
| [
{
"version": "v1",
"created": "Mon, 20 Jun 2016 15:00:59 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Jun 2016 08:27:52 GMT"
},
{
"version": "v3",
"created": "Sun, 28 May 2017 23:57:32 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Jiang",
"Yazhen",
""
],
[
"Skufca",
"Joseph",
""
],
[
"Sun",
"Jie",
""
]
] | TITLE: BiFold visualization of bipartite datasets
ABSTRACT: The emerging domain of data-enabled science necessitates development of
algorithms and tools for knowledge discovery. Human interaction with data
through well-constructed graphical representation can take special advantage of
our visual ability to identify patterns. We develop a data visualization
framework, called BiFold, for exploratory analysis of bipartite datasets that
describe binary relationships between groups of objects. Typical data examples
would include voting records, organizational memberships, and pairwise
associations, or other binary datasets. BiFold provides a low dimensional
embedding of data that represents similarity by visual nearness, analogous to
Multidimensional Scaling (MDS). The unique and new feature of BiFold is its
ability to simultaneously capture both within-group and between-group
relationships among objects, enhancing knowledge discovery. We benchmark BiFold
using the {\it Southern Women Dataset}, where social groups are now visually
evident. We construct BiFold plots for two US voting datasets: For the
presidential election outcomes since 1976, BiFold illustrates the evolving
geopolitical structures that underlie these election results. For Senate
congressional voting, BiFold identifies a partisan coordinate, separating
senators into two parties while simultaneously visualizing a
bipartisan-coalition coordinate which captures the ultimate fate of the bills
(pass/fail). Finally, we consider a global cuisine dataset of the association
between recipes and food ingredients. BiFold allows us to visually compare and
contrast cuisines while also allowing identification of signature ingredients
of individual cuisines.
| new_dataset | 0.511034 |
1608.00508 | Paul Michel | Paul Michel, Okko R\"as\"anen, Roland Thiolli\`ere, Emmanuel Dupoux | Blind phoneme segmentation with temporal prediction errors | 7 pages 3 figures. Presented at ACL SRW 2017 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phonemic segmentation of speech is a critical step of speech recognition
systems. We propose a novel unsupervised algorithm based on sequence prediction
models such as Markov chains and recurrent neural network. Our approach
consists in analyzing the error profile of a model trained to predict speech
features frame-by-frame. Specifically, we try to learn the dynamics of speech
in the MFCC space and hypothesize boundaries from local maxima in the
prediction error. We evaluate our system on the TIMIT dataset, with
improvements over similar methods.
| [
{
"version": "v1",
"created": "Mon, 1 Aug 2016 17:51:03 GMT"
},
{
"version": "v2",
"created": "Sat, 27 May 2017 04:01:13 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Michel",
"Paul",
""
],
[
"Räsänen",
"Okko",
""
],
[
"Thiollière",
"Roland",
""
],
[
"Dupoux",
"Emmanuel",
""
]
] | TITLE: Blind phoneme segmentation with temporal prediction errors
ABSTRACT: Phonemic segmentation of speech is a critical step of speech recognition
systems. We propose a novel unsupervised algorithm based on sequence prediction
models such as Markov chains and recurrent neural network. Our approach
consists in analyzing the error profile of a model trained to predict speech
features frame-by-frame. Specifically, we try to learn the dynamics of speech
in the MFCC space and hypothesize boundaries from local maxima in the
prediction error. We evaluate our system on the TIMIT dataset, with
improvements over similar methods.
| no_new_dataset | 0.947088 |
1610.03466 | Xianzhi Du | Xianzhi Du and Mostafa El-Khamy and Jungwon Lee and Larry S. Davis | Fused DNN: A deep neural network fusion approach to fast and robust
pedestrian detection | WACV 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a deep neural network fusion architecture for fast and robust
pedestrian detection. The proposed network fusion architecture allows for
parallel processing of multiple networks for speed. A single shot deep
convolutional network is trained as a object detector to generate all possible
pedestrian candidates of different sizes and occlusions. This network outputs a
large variety of pedestrian candidates to cover the majority of ground-truth
pedestrians while also introducing a large number of false positives. Next,
multiple deep neural networks are used in parallel for further refinement of
these pedestrian candidates. We introduce a soft-rejection based network fusion
method to fuse the soft metrics from all networks together to generate the
final confidence scores. Our method performs better than existing
state-of-the-arts, especially when detecting small-size and occluded
pedestrians. Furthermore, we propose a method for integrating pixel-wise
semantic segmentation network into the network fusion architecture as a
reinforcement to the pedestrian detector. The approach outperforms
state-of-the-art methods on most protocols on Caltech Pedestrian dataset, with
significant boosts on several protocols. It is also faster than all other
methods.
| [
{
"version": "v1",
"created": "Tue, 11 Oct 2016 18:59:12 GMT"
},
{
"version": "v2",
"created": "Sun, 28 May 2017 15:45:56 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Du",
"Xianzhi",
""
],
[
"El-Khamy",
"Mostafa",
""
],
[
"Lee",
"Jungwon",
""
],
[
"Davis",
"Larry S.",
""
]
] | TITLE: Fused DNN: A deep neural network fusion approach to fast and robust
pedestrian detection
ABSTRACT: We propose a deep neural network fusion architecture for fast and robust
pedestrian detection. The proposed network fusion architecture allows for
parallel processing of multiple networks for speed. A single shot deep
convolutional network is trained as a object detector to generate all possible
pedestrian candidates of different sizes and occlusions. This network outputs a
large variety of pedestrian candidates to cover the majority of ground-truth
pedestrians while also introducing a large number of false positives. Next,
multiple deep neural networks are used in parallel for further refinement of
these pedestrian candidates. We introduce a soft-rejection based network fusion
method to fuse the soft metrics from all networks together to generate the
final confidence scores. Our method performs better than existing
state-of-the-arts, especially when detecting small-size and occluded
pedestrians. Furthermore, we propose a method for integrating pixel-wise
semantic segmentation network into the network fusion architecture as a
reinforcement to the pedestrian detector. The approach outperforms
state-of-the-art methods on most protocols on Caltech Pedestrian dataset, with
significant boosts on several protocols. It is also faster than all other
methods.
| no_new_dataset | 0.950503 |
1611.04878 | Yeounoh Chung | Yeounoh Chung, Sanjay Krishnan, Tim Kraska | A Data Quality Metric (DQM): How to Estimate The Number of Undetected
Errors in Data Sets | To appear in VLDB 2017 | null | null | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data cleaning, whether manual or algorithmic, is rarely perfect leaving a
dataset with an unknown number of false positives and false negatives after
cleaning. In many scenarios, quantifying the number of remaining errors is
challenging because our data integrity rules themselves may be incomplete, or
the available gold-standard datasets may be too small to extrapolate. As the
use of inherently fallible crowds becomes more prevalent in data cleaning
problems, it is important to have estimators to quantify the extent of such
errors. We propose novel species estimators to estimate the number of distinct
remaining errors in a dataset after it has been cleaned by a set of crowd
workers -- essentially, quantifying the utility of hiring additional workers to
clean the dataset. This problem requires new estimators that are robust to
false positives and false negatives, and we empirically show on three
real-world datasets that existing species estimators are unstable for this
problem, while our proposed techniques quickly converge.
| [
{
"version": "v1",
"created": "Tue, 15 Nov 2016 15:00:53 GMT"
},
{
"version": "v2",
"created": "Sat, 11 Mar 2017 04:46:38 GMT"
},
{
"version": "v3",
"created": "Fri, 26 May 2017 18:24:26 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Chung",
"Yeounoh",
""
],
[
"Krishnan",
"Sanjay",
""
],
[
"Kraska",
"Tim",
""
]
] | TITLE: A Data Quality Metric (DQM): How to Estimate The Number of Undetected
Errors in Data Sets
ABSTRACT: Data cleaning, whether manual or algorithmic, is rarely perfect leaving a
dataset with an unknown number of false positives and false negatives after
cleaning. In many scenarios, quantifying the number of remaining errors is
challenging because our data integrity rules themselves may be incomplete, or
the available gold-standard datasets may be too small to extrapolate. As the
use of inherently fallible crowds becomes more prevalent in data cleaning
problems, it is important to have estimators to quantify the extent of such
errors. We propose novel species estimators to estimate the number of distinct
remaining errors in a dataset after it has been cleaned by a set of crowd
workers -- essentially, quantifying the utility of hiring additional workers to
clean the dataset. This problem requires new estimators that are robust to
false positives and false negatives, and we empirically show on three
real-world datasets that existing species estimators are unstable for this
problem, while our proposed techniques quickly converge.
| no_new_dataset | 0.9455 |
1612.03530 | Diqi Chen | Diqi Chen, Yizhou Wang, Tianfu Wu, Wen Gao | An Attention-Driven Approach of No-Reference Image Quality Assessment | 9 pages, 7 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present a novel method of no-reference image quality
assessment (NR-IQA), which is to predict the perceptual quality score of a
given image without using any reference image. The proposed method harnesses
three functions (i) the visual attention mechanism, which affects many aspects
of visual perception including image quality assessment, however, is overlooked
in the NR-IQA literature. The method assumes that the fixation areas on an
image contain key information to the process of IQA. (ii) the robust averaging
strategy, which is a means \--- supported by psychology studies \--- to
integrating multiple/step-wise evidence to make a final perceptual judgment.
(iii) the multi-task learning, which is believed to be an effectual means to
shape representation learning and could result in a more generalized model.
To exploit the synergy of the three, we consider the NR-IQA as a dynamic
perception process, in which the model samples a sequence of "informative"
areas and aggregates the information to learn a representation for the tasks of
jointly predicting the image quality score and the distortion type.
The model learning is implemented by a reinforcement strategy, in which the
rewards of both tasks guide the learning of the optimal sampling policy to
acquire the "task-informative" image regions so that the predictions can be
made accurately and efficiently (in terms of the sampling steps). The
reinforcement learning is realized by a deep network with the policy gradient
method and trained through back-propagation.
In experiments, the model is tested on the TID2008 dataset and it outperforms
several state-of-the-art methods. Furthermore, the model is very efficient in
the sense that a small number of fixations are used in NR-IQA.
| [
{
"version": "v1",
"created": "Mon, 12 Dec 2016 03:25:35 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Mar 2017 01:46:45 GMT"
},
{
"version": "v3",
"created": "Mon, 29 May 2017 02:42:28 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Chen",
"Diqi",
""
],
[
"Wang",
"Yizhou",
""
],
[
"Wu",
"Tianfu",
""
],
[
"Gao",
"Wen",
""
]
] | TITLE: An Attention-Driven Approach of No-Reference Image Quality Assessment
ABSTRACT: In this paper, we present a novel method of no-reference image quality
assessment (NR-IQA), which is to predict the perceptual quality score of a
given image without using any reference image. The proposed method harnesses
three functions (i) the visual attention mechanism, which affects many aspects
of visual perception including image quality assessment, however, is overlooked
in the NR-IQA literature. The method assumes that the fixation areas on an
image contain key information to the process of IQA. (ii) the robust averaging
strategy, which is a means \--- supported by psychology studies \--- to
integrating multiple/step-wise evidence to make a final perceptual judgment.
(iii) the multi-task learning, which is believed to be an effectual means to
shape representation learning and could result in a more generalized model.
To exploit the synergy of the three, we consider the NR-IQA as a dynamic
perception process, in which the model samples a sequence of "informative"
areas and aggregates the information to learn a representation for the tasks of
jointly predicting the image quality score and the distortion type.
The model learning is implemented by a reinforcement strategy, in which the
rewards of both tasks guide the learning of the optimal sampling policy to
acquire the "task-informative" image regions so that the predictions can be
made accurately and efficiently (in terms of the sampling steps). The
reinforcement learning is realized by a deep network with the policy gradient
method and trained through back-propagation.
In experiments, the model is tested on the TID2008 dataset and it outperforms
several state-of-the-art methods. Furthermore, the model is very efficient in
the sense that a small number of fixations are used in NR-IQA.
| no_new_dataset | 0.94474 |
1612.06530 | Shaodi You | Shijie Zhang, Lizhen Qu, Shaodi You, Zhenglu Yang, Jiawan Zhang | Automatic Generation of Grounded Visual Questions | VQA | IJCAI 2017 | null | null | cs.CV cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose the first model to be able to generate visually
grounded questions with diverse types for a single image. Visual question
generation is an emerging topic which aims to ask questions in natural language
based on visual input. To the best of our knowledge, it lacks automatic methods
to generate meaningful questions with various types for the same visual input.
To circumvent the problem, we propose a model that automatically generates
visually grounded questions with varying types. Our model takes as input both
images and the captions generated by a dense caption model, samples the most
probable question types, and generates the questions in sequel. The
experimental results on two real world datasets show that our model outperforms
the strongest baseline in terms of both correctness and diversity with a wide
margin.
| [
{
"version": "v1",
"created": "Tue, 20 Dec 2016 07:20:16 GMT"
},
{
"version": "v2",
"created": "Mon, 29 May 2017 12:54:35 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Zhang",
"Shijie",
""
],
[
"Qu",
"Lizhen",
""
],
[
"You",
"Shaodi",
""
],
[
"Yang",
"Zhenglu",
""
],
[
"Zhang",
"Jiawan",
""
]
] | TITLE: Automatic Generation of Grounded Visual Questions
ABSTRACT: In this paper, we propose the first model to be able to generate visually
grounded questions with diverse types for a single image. Visual question
generation is an emerging topic which aims to ask questions in natural language
based on visual input. To the best of our knowledge, it lacks automatic methods
to generate meaningful questions with various types for the same visual input.
To circumvent the problem, we propose a model that automatically generates
visually grounded questions with varying types. Our model takes as input both
images and the captions generated by a dense caption model, samples the most
probable question types, and generates the questions in sequel. The
experimental results on two real world datasets show that our model outperforms
the strongest baseline in terms of both correctness and diversity with a wide
margin.
| no_new_dataset | 0.952131 |
1702.04459 | Dianhui Wang | Dianhui Wang, Ming Li | Robust Stochastic Configuration Networks with Kernel Density Estimation | 14 pages | null | null | null | cs.NE cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural networks have been widely used as predictive models to fit data
distribution, and they could be implemented through learning a collection of
samples. In many applications, however, the given dataset may contain noisy
samples or outliers which may result in a poor learner model in terms of
generalization. This paper contributes to a development of robust stochastic
configuration networks (RSCNs) for resolving uncertain data regression
problems. RSCNs are built on original stochastic configuration networks with
weighted least squares method for evaluating the output weights, and the input
weights and biases are incrementally and randomly generated by satisfying with
a set of inequality constrains. The kernel density estimation (KDE) method is
employed to set the penalty weights for each training samples, so that some
negative impacts, caused by noisy data or outliers, on the resulting learner
model can be reduced. The alternating optimization technique is applied for
updating a RSCN model with improved penalty weights computed from the kernel
density estimation function. Performance evaluation is carried out by a
function approximation, four benchmark datasets and a case study on engineering
application. Comparisons to other robust randomised neural modelling
techniques, including the probabilistic robust learning algorithm for neural
networks with random weights and improved RVFL networks, indicate that the
proposed RSCNs with KDE perform favourably and demonstrate good potential for
real-world applications.
| [
{
"version": "v1",
"created": "Wed, 15 Feb 2017 03:54:29 GMT"
},
{
"version": "v2",
"created": "Mon, 29 May 2017 15:29:47 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Wang",
"Dianhui",
""
],
[
"Li",
"Ming",
""
]
] | TITLE: Robust Stochastic Configuration Networks with Kernel Density Estimation
ABSTRACT: Neural networks have been widely used as predictive models to fit data
distribution, and they could be implemented through learning a collection of
samples. In many applications, however, the given dataset may contain noisy
samples or outliers which may result in a poor learner model in terms of
generalization. This paper contributes to a development of robust stochastic
configuration networks (RSCNs) for resolving uncertain data regression
problems. RSCNs are built on original stochastic configuration networks with
weighted least squares method for evaluating the output weights, and the input
weights and biases are incrementally and randomly generated by satisfying with
a set of inequality constrains. The kernel density estimation (KDE) method is
employed to set the penalty weights for each training samples, so that some
negative impacts, caused by noisy data or outliers, on the resulting learner
model can be reduced. The alternating optimization technique is applied for
updating a RSCN model with improved penalty weights computed from the kernel
density estimation function. Performance evaluation is carried out by a
function approximation, four benchmark datasets and a case study on engineering
application. Comparisons to other robust randomised neural modelling
techniques, including the probabilistic robust learning algorithm for neural
networks with random weights and improved RVFL networks, indicate that the
proposed RSCNs with KDE perform favourably and demonstrate good potential for
real-world applications.
| no_new_dataset | 0.947866 |
1702.07817 | Jianshu Chen | Yu Liu, Jianshu Chen, Li Deng | Unsupervised Sequence Classification using Sequential Output Statistics | All authors contributed equally to the paper. 17 pages, 7 figures and
2 tables | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider learning a sequence classifier without labeled data by using
sequential output statistics. The problem is highly valuable since obtaining
labels in training data is often costly, while the sequential output statistics
(e.g., language models) could be obtained independently of input data and thus
with low or no cost. To address the problem, we propose an unsupervised
learning cost function and study its properties. We show that, compared to
earlier works, it is less inclined to be stuck in trivial solutions and avoids
the need for a strong generative model. Although it is harder to optimize in
its functional form, a stochastic primal-dual gradient method is developed to
effectively solve the problem. Experiment results on real-world datasets
demonstrate that the new unsupervised learning method gives drastically lower
errors than other baseline methods. Specifically, it reaches test errors about
twice of those obtained by fully supervised learning.
| [
{
"version": "v1",
"created": "Sat, 25 Feb 2017 01:55:38 GMT"
},
{
"version": "v2",
"created": "Fri, 26 May 2017 18:30:24 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Liu",
"Yu",
""
],
[
"Chen",
"Jianshu",
""
],
[
"Deng",
"Li",
""
]
] | TITLE: Unsupervised Sequence Classification using Sequential Output Statistics
ABSTRACT: We consider learning a sequence classifier without labeled data by using
sequential output statistics. The problem is highly valuable since obtaining
labels in training data is often costly, while the sequential output statistics
(e.g., language models) could be obtained independently of input data and thus
with low or no cost. To address the problem, we propose an unsupervised
learning cost function and study its properties. We show that, compared to
earlier works, it is less inclined to be stuck in trivial solutions and avoids
the need for a strong generative model. Although it is harder to optimize in
its functional form, a stochastic primal-dual gradient method is developed to
effectively solve the problem. Experiment results on real-world datasets
demonstrate that the new unsupervised learning method gives drastically lower
errors than other baseline methods. Specifically, it reaches test errors about
twice of those obtained by fully supervised learning.
| no_new_dataset | 0.951639 |
1704.00616 | Mohammadreza Zolfaghari | Mohammadreza Zolfaghari, Gabriel L. Oliveira, Nima Sedaghat, and
Thomas Brox | Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance
for Action Classification and Detection | 10 pages, 7 figures, ICCV 2017 submission | null | null | null | cs.CV cs.AI cs.HC cs.MM cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | General human action recognition requires understanding of various visual
cues. In this paper, we propose a network architecture that computes and
integrates the most important visual cues for action recognition: pose, motion,
and the raw images. For the integration, we introduce a Markov chain model
which adds cues successively. The resulting approach is efficient and
applicable to action classification as well as to spatial and temporal action
localization. The two contributions clearly improve the performance over
respective baselines. The overall approach achieves state-of-the-art action
classification performance on HMDB51, J-HMDB and NTU RGB+D datasets. Moreover,
it yields state-of-the-art spatio-temporal action localization results on
UCF101 and J-HMDB.
| [
{
"version": "v1",
"created": "Mon, 3 Apr 2017 14:29:40 GMT"
},
{
"version": "v2",
"created": "Fri, 26 May 2017 18:40:14 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Zolfaghari",
"Mohammadreza",
""
],
[
"Oliveira",
"Gabriel L.",
""
],
[
"Sedaghat",
"Nima",
""
],
[
"Brox",
"Thomas",
""
]
] | TITLE: Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance
for Action Classification and Detection
ABSTRACT: General human action recognition requires understanding of various visual
cues. In this paper, we propose a network architecture that computes and
integrates the most important visual cues for action recognition: pose, motion,
and the raw images. For the integration, we introduce a Markov chain model
which adds cues successively. The resulting approach is efficient and
applicable to action classification as well as to spatial and temporal action
localization. The two contributions clearly improve the performance over
respective baselines. The overall approach achieves state-of-the-art action
classification performance on HMDB51, J-HMDB and NTU RGB+D datasets. Moreover,
it yields state-of-the-art spatio-temporal action localization results on
UCF101 and J-HMDB.
| no_new_dataset | 0.949716 |
1704.02801 | Ahmed Alaa | Ahmed M. Alaa and Mihaela van der Schaar | Bayesian Inference of Individualized Treatment Effects using Multi-task
Gaussian Processes | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predicated on the increasing abundance of electronic health records, we
investi- gate the problem of inferring individualized treatment effects using
observational data. Stemming from the potential outcomes model, we propose a
novel multi- task learning framework in which factual and counterfactual
outcomes are mod- eled as the outputs of a function in a vector-valued
reproducing kernel Hilbert space (vvRKHS). We develop a nonparametric Bayesian
method for learning the treatment effects using a multi-task Gaussian process
(GP) with a linear coregion- alization kernel as a prior over the vvRKHS. The
Bayesian approach allows us to compute individualized measures of confidence in
our estimates via pointwise credible intervals, which are crucial for realizing
the full potential of precision medicine. The impact of selection bias is
alleviated via a risk-based empirical Bayes method for adapting the multi-task
GP prior, which jointly minimizes the empirical error in factual outcomes and
the uncertainty in (unobserved) counter- factual outcomes. We conduct
experiments on observational datasets for an inter- ventional social program
applied to premature infants, and a left ventricular assist device applied to
cardiac patients wait-listed for a heart transplant. In both experi- ments, we
show that our method significantly outperforms the state-of-the-art.
| [
{
"version": "v1",
"created": "Mon, 10 Apr 2017 11:03:36 GMT"
},
{
"version": "v2",
"created": "Sun, 28 May 2017 13:29:58 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Alaa",
"Ahmed M.",
""
],
[
"van der Schaar",
"Mihaela",
""
]
] | TITLE: Bayesian Inference of Individualized Treatment Effects using Multi-task
Gaussian Processes
ABSTRACT: Predicated on the increasing abundance of electronic health records, we
investi- gate the problem of inferring individualized treatment effects using
observational data. Stemming from the potential outcomes model, we propose a
novel multi- task learning framework in which factual and counterfactual
outcomes are mod- eled as the outputs of a function in a vector-valued
reproducing kernel Hilbert space (vvRKHS). We develop a nonparametric Bayesian
method for learning the treatment effects using a multi-task Gaussian process
(GP) with a linear coregion- alization kernel as a prior over the vvRKHS. The
Bayesian approach allows us to compute individualized measures of confidence in
our estimates via pointwise credible intervals, which are crucial for realizing
the full potential of precision medicine. The impact of selection bias is
alleviated via a risk-based empirical Bayes method for adapting the multi-task
GP prior, which jointly minimizes the empirical error in factual outcomes and
the uncertainty in (unobserved) counter- factual outcomes. We conduct
experiments on observational datasets for an inter- ventional social program
applied to premature infants, and a left ventricular assist device applied to
cardiac patients wait-listed for a heart transplant. In both experi- ments, we
show that our method significantly outperforms the state-of-the-art.
| no_new_dataset | 0.947817 |
1705.01921 | Liliang Ren | Liliang Ren | Recurrent Soft Attention Model for Common Object Recognition | 5 pages, 4 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose the Recurrent Soft Attention Model, which integrates the visual
attention from the original image to a LSTM memory cell through a down-sample
network. The model recurrently transmits visual attention to the memory cells
for glimpse mask generation, which is a more natural way for attention
integration and exploitation in general object detection and recognition
problem. We test our model under the metric of the top-1 accuracy on the
CIFAR-10 dataset. The experiment shows that our down-sample network and
feedback mechanism plays an effective role among the whole network structure.
| [
{
"version": "v1",
"created": "Thu, 4 May 2017 17:27:42 GMT"
},
{
"version": "v2",
"created": "Mon, 29 May 2017 07:02:52 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Ren",
"Liliang",
""
]
] | TITLE: Recurrent Soft Attention Model for Common Object Recognition
ABSTRACT: We propose the Recurrent Soft Attention Model, which integrates the visual
attention from the original image to a LSTM memory cell through a down-sample
network. The model recurrently transmits visual attention to the memory cells
for glimpse mask generation, which is a more natural way for attention
integration and exploitation in general object detection and recognition
problem. We test our model under the metric of the top-1 accuracy on the
CIFAR-10 dataset. The experiment shows that our down-sample network and
feedback mechanism plays an effective role among the whole network structure.
| no_new_dataset | 0.952175 |
1705.02758 | Xiu-Shen Wei | Xiu-Shen Wei, Chen-Lin Zhang, Yao Li, Chen-Wei Xie, Jianxin Wu,
Chunhua Shen, Zhi-Hua Zhou | Deep Descriptor Transforming for Image Co-Localization | Accepted by IJCAI 2017 | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reusable model design becomes desirable with the rapid expansion of machine
learning applications. In this paper, we focus on the reusability of
pre-trained deep convolutional models. Specifically, different from treating
pre-trained models as feature extractors, we reveal more treasures beneath
convolutional layers, i.e., the convolutional activations could act as a
detector for the common object in the image co-localization problem. We propose
a simple but effective method, named Deep Descriptor Transforming (DDT), for
evaluating the correlations of descriptors and then obtaining the
category-consistent regions, which can accurately locate the common object in a
set of images. Empirical studies validate the effectiveness of the proposed DDT
method. On benchmark image co-localization datasets, DDT consistently
outperforms existing state-of-the-art methods by a large margin. Moreover, DDT
also demonstrates good generalization ability for unseen categories and
robustness for dealing with noisy data.
| [
{
"version": "v1",
"created": "Mon, 8 May 2017 06:52:44 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Wei",
"Xiu-Shen",
""
],
[
"Zhang",
"Chen-Lin",
""
],
[
"Li",
"Yao",
""
],
[
"Xie",
"Chen-Wei",
""
],
[
"Wu",
"Jianxin",
""
],
[
"Shen",
"Chunhua",
""
],
[
"Zhou",
"Zhi-Hua",
""
]
] | TITLE: Deep Descriptor Transforming for Image Co-Localization
ABSTRACT: Reusable model design becomes desirable with the rapid expansion of machine
learning applications. In this paper, we focus on the reusability of
pre-trained deep convolutional models. Specifically, different from treating
pre-trained models as feature extractors, we reveal more treasures beneath
convolutional layers, i.e., the convolutional activations could act as a
detector for the common object in the image co-localization problem. We propose
a simple but effective method, named Deep Descriptor Transforming (DDT), for
evaluating the correlations of descriptors and then obtaining the
category-consistent regions, which can accurately locate the common object in a
set of images. Empirical studies validate the effectiveness of the proposed DDT
method. On benchmark image co-localization datasets, DDT consistently
outperforms existing state-of-the-art methods by a large margin. Moreover, DDT
also demonstrates good generalization ability for unseen categories and
robustness for dealing with noisy data.
| no_new_dataset | 0.947039 |
1705.08111 | Benjamin Gutierrez Becker | Benjam\'in Guti\'errez and Lo\"ic Peter and Tassilo Klein and
Christian Wachinger | A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical
Data | MICCAI 2017 Proceedings | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the availability of big medical image data, the selection of an adequate
training set is becoming more important to address the heterogeneity of
different datasets. Simply including all the data does not only incur high
processing costs but can even harm the prediction. We formulate the smart and
efficient selection of a training dataset from big medical image data as a
multi-armed bandit problem, solved by Thompson sampling. Our method assumes
that image features are not available at the time of the selection of the
samples, and therefore relies only on meta information associated with the
images. Our strategy simultaneously exploits data sources with high chances of
yielding useful samples and explores new data regions. For our evaluation, we
focus on the application of estimating the age from a brain MRI. Our results on
7,250 subjects from 10 datasets show that our approach leads to higher accuracy
while only requiring a fraction of the training data.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 07:51:54 GMT"
},
{
"version": "v2",
"created": "Mon, 29 May 2017 12:50:19 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Gutiérrez",
"Benjamín",
""
],
[
"Peter",
"Loïc",
""
],
[
"Klein",
"Tassilo",
""
],
[
"Wachinger",
"Christian",
""
]
] | TITLE: A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical
Data
ABSTRACT: With the availability of big medical image data, the selection of an adequate
training set is becoming more important to address the heterogeneity of
different datasets. Simply including all the data does not only incur high
processing costs but can even harm the prediction. We formulate the smart and
efficient selection of a training dataset from big medical image data as a
multi-armed bandit problem, solved by Thompson sampling. Our method assumes
that image features are not available at the time of the selection of the
samples, and therefore relies only on meta information associated with the
images. Our strategy simultaneously exploits data sources with high chances of
yielding useful samples and explores new data regions. For our evaluation, we
focus on the application of estimating the age from a brain MRI. Our results on
7,250 subjects from 10 datasets show that our approach leads to higher accuracy
while only requiring a fraction of the training data.
| no_new_dataset | 0.950824 |
1705.09724 | Iroro Orife | Shane Walker, Morten Pedersen, Iroro Orife and Jason Flaks | Semi-Supervised Model Training for Unbounded Conversational Speech
Recognition | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For conversational large-vocabulary continuous speech recognition (LVCSR)
tasks, up to about two thousand hours of audio is commonly used to train state
of the art models. Collection of labeled conversational audio however, is
prohibitively expensive, laborious and error-prone. Furthermore, academic
corpora like Fisher English (2004) or Switchboard (1992) are inadequate to
train models with sufficient accuracy in the unbounded space of conversational
speech. These corpora are also timeworn due to dated acoustic telephony
features and the rapid advancement of colloquial vocabulary and idiomatic
speech over the last decades. Utilizing the colossal scale of our unlabeled
telephony dataset, we propose a technique to construct a modern, high quality
conversational speech training corpus on the order of hundreds of millions of
utterances (or tens of thousands of hours) for both acoustic and language model
training. We describe the data collection, selection and training, evaluating
the results of our updated speech recognition system on a test corpus of 7K
manually transcribed utterances. We show relative word error rate (WER)
reductions of {35%, 19%} on {agent, caller} utterances over our seed model and
5% absolute WER improvements over IBM Watson STT on this conversational speech
task.
| [
{
"version": "v1",
"created": "Fri, 26 May 2017 21:10:15 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Walker",
"Shane",
""
],
[
"Pedersen",
"Morten",
""
],
[
"Orife",
"Iroro",
""
],
[
"Flaks",
"Jason",
""
]
] | TITLE: Semi-Supervised Model Training for Unbounded Conversational Speech
Recognition
ABSTRACT: For conversational large-vocabulary continuous speech recognition (LVCSR)
tasks, up to about two thousand hours of audio is commonly used to train state
of the art models. Collection of labeled conversational audio however, is
prohibitively expensive, laborious and error-prone. Furthermore, academic
corpora like Fisher English (2004) or Switchboard (1992) are inadequate to
train models with sufficient accuracy in the unbounded space of conversational
speech. These corpora are also timeworn due to dated acoustic telephony
features and the rapid advancement of colloquial vocabulary and idiomatic
speech over the last decades. Utilizing the colossal scale of our unlabeled
telephony dataset, we propose a technique to construct a modern, high quality
conversational speech training corpus on the order of hundreds of millions of
utterances (or tens of thousands of hours) for both acoustic and language model
training. We describe the data collection, selection and training, evaluating
the results of our updated speech recognition system on a test corpus of 7K
manually transcribed utterances. We show relative word error rate (WER)
reductions of {35%, 19%} on {agent, caller} utterances over our seed model and
5% absolute WER improvements over IBM Watson STT on this conversational speech
task.
| new_dataset | 0.955775 |
1705.09800 | Guy Uziel | Guy Uziel and Ran El-Yaniv | Growth-Optimal Portfolio Selection under CVaR Constraints | null | null | null | null | q-fin.MF cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Online portfolio selection research has so far focused mainly on minimizing
regret defined in terms of wealth growth. Practical financial decision making,
however, is deeply concerned with both wealth and risk. We consider online
learning of portfolios of stocks whose prices are governed by arbitrary
(unknown) stationary and ergodic processes, where the goal is to maximize
wealth while keeping the conditional value at risk (CVaR) below a desired
threshold. We characterize the asymptomatically optimal risk-adjusted
performance and present an investment strategy whose portfolios are guaranteed
to achieve the asymptotic optimal solution while fulfilling the desired risk
constraint. We also numerically demonstrate and validate the viability of our
method on standard datasets.
| [
{
"version": "v1",
"created": "Sat, 27 May 2017 10:27:03 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Uziel",
"Guy",
""
],
[
"El-Yaniv",
"Ran",
""
]
] | TITLE: Growth-Optimal Portfolio Selection under CVaR Constraints
ABSTRACT: Online portfolio selection research has so far focused mainly on minimizing
regret defined in terms of wealth growth. Practical financial decision making,
however, is deeply concerned with both wealth and risk. We consider online
learning of portfolios of stocks whose prices are governed by arbitrary
(unknown) stationary and ergodic processes, where the goal is to maximize
wealth while keeping the conditional value at risk (CVaR) below a desired
threshold. We characterize the asymptomatically optimal risk-adjusted
performance and present an investment strategy whose portfolios are guaranteed
to achieve the asymptotic optimal solution while fulfilling the desired risk
constraint. We also numerically demonstrate and validate the viability of our
method on standard datasets.
| no_new_dataset | 0.943971 |
1705.09888 | Peng Xu | Peng Xu, Qiyue Yin, Yongye Huang, Yi-Zhe Song, Zhanyu Ma, Liang Wang,
Tao Xiang, W. Bastiaan Kleijn, Jun Guo | Cross-modal Subspace Learning for Fine-grained Sketch-based Image
Retrieval | Accepted by Neurocomputing | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sketch-based image retrieval (SBIR) is challenging due to the inherent
domain-gap between sketch and photo. Compared with pixel-perfect depictions of
photos, sketches are iconic renderings of the real world with highly abstract.
Therefore, matching sketch and photo directly using low-level visual clues are
unsufficient, since a common low-level subspace that traverses semantically
across the two modalities is non-trivial to establish. Most existing SBIR
studies do not directly tackle this cross-modal problem. This naturally
motivates us to explore the effectiveness of cross-modal retrieval methods in
SBIR, which have been applied in the image-text matching successfully. In this
paper, we introduce and compare a series of state-of-the-art cross-modal
subspace learning methods and benchmark them on two recently released
fine-grained SBIR datasets. Through thorough examination of the experimental
results, we have demonstrated that the subspace learning can effectively model
the sketch-photo domain-gap. In addition we draw a few key insights to drive
future research.
| [
{
"version": "v1",
"created": "Sun, 28 May 2017 03:45:26 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Xu",
"Peng",
""
],
[
"Yin",
"Qiyue",
""
],
[
"Huang",
"Yongye",
""
],
[
"Song",
"Yi-Zhe",
""
],
[
"Ma",
"Zhanyu",
""
],
[
"Wang",
"Liang",
""
],
[
"Xiang",
"Tao",
""
],
[
"Kleijn",
"W. Bastiaan",
""
],
[
"Guo",
"Jun",
""
]
] | TITLE: Cross-modal Subspace Learning for Fine-grained Sketch-based Image
Retrieval
ABSTRACT: Sketch-based image retrieval (SBIR) is challenging due to the inherent
domain-gap between sketch and photo. Compared with pixel-perfect depictions of
photos, sketches are iconic renderings of the real world with highly abstract.
Therefore, matching sketch and photo directly using low-level visual clues are
unsufficient, since a common low-level subspace that traverses semantically
across the two modalities is non-trivial to establish. Most existing SBIR
studies do not directly tackle this cross-modal problem. This naturally
motivates us to explore the effectiveness of cross-modal retrieval methods in
SBIR, which have been applied in the image-text matching successfully. In this
paper, we introduce and compare a series of state-of-the-art cross-modal
subspace learning methods and benchmark them on two recently released
fine-grained SBIR datasets. Through thorough examination of the experimental
results, we have demonstrated that the subspace learning can effectively model
the sketch-photo domain-gap. In addition we draw a few key insights to drive
future research.
| no_new_dataset | 0.944689 |
1705.09892 | Chunhua Shen | Bohan Zhuang, Qi Wu, Chunhua Shen, Ian Reid, Anton van den Hengel | Care about you: towards large-scale human-centric visual relationship
detection | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visual relationship detection aims to capture interactions between pairs of
objects in images. Relationships between objects and humans represent a
particularly important subset of this problem, with implications for challenges
such as understanding human behaviour, and identifying affordances, amongst
others. In addressing this problem we first construct a large-scale
human-centric visual relationship detection dataset (HCVRD), which provides
many more types of relationship annotation (nearly 10K categories) than the
previous released datasets.
This large label space better reflects the reality of human-object
interactions, but gives rise to a long-tail distribution problem, which in turn
demands a zero-shot approach to labels appearing only in the test set. This is
the first time this issue has been addressed. We propose a webly-supervised
approach to these problems and demonstrate that the proposed model provides a
strong baseline on our HCVRD dataset.
| [
{
"version": "v1",
"created": "Sun, 28 May 2017 05:53:38 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Zhuang",
"Bohan",
""
],
[
"Wu",
"Qi",
""
],
[
"Shen",
"Chunhua",
""
],
[
"Reid",
"Ian",
""
],
[
"Hengel",
"Anton van den",
""
]
] | TITLE: Care about you: towards large-scale human-centric visual relationship
detection
ABSTRACT: Visual relationship detection aims to capture interactions between pairs of
objects in images. Relationships between objects and humans represent a
particularly important subset of this problem, with implications for challenges
such as understanding human behaviour, and identifying affordances, amongst
others. In addressing this problem we first construct a large-scale
human-centric visual relationship detection dataset (HCVRD), which provides
many more types of relationship annotation (nearly 10K categories) than the
previous released datasets.
This large label space better reflects the reality of human-object
interactions, but gives rise to a long-tail distribution problem, which in turn
demands a zero-shot approach to labels appearing only in the test set. This is
the first time this issue has been addressed. We propose a webly-supervised
approach to these problems and demonstrate that the proposed model provides a
strong baseline on our HCVRD dataset.
| new_dataset | 0.959687 |
1705.09906 | Haichao Zhang | Haichao Zhang, Haonan Yu, and Wei Xu | Listen, Interact and Talk: Learning to Speak via Interaction | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the long-term goals of artificial intelligence is to build an agent
that can communicate intelligently with human in natural language. Most
existing work on natural language learning relies heavily on training over a
pre-collected dataset with annotated labels, leading to an agent that
essentially captures the statistics of the fixed external training data. As the
training data is essentially a static snapshot representation of the knowledge
from the annotator, the agent trained this way is limited in adaptiveness and
generalization of its behavior. Moreover, this is very different from the
language learning process of humans, where language is acquired during
communication by taking speaking action and learning from the consequences of
speaking action in an interactive manner. This paper presents an interactive
setting for grounded natural language learning, where an agent learns natural
language by interacting with a teacher and learning from feedback, thus
learning and improving language skills while taking part in the conversation.
To achieve this goal, we propose a model which incorporates both imitation and
reinforcement by leveraging jointly sentence and reward feedbacks from the
teacher. Experiments are conducted to validate the effectiveness of the
proposed approach.
| [
{
"version": "v1",
"created": "Sun, 28 May 2017 07:48:14 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Zhang",
"Haichao",
""
],
[
"Yu",
"Haonan",
""
],
[
"Xu",
"Wei",
""
]
] | TITLE: Listen, Interact and Talk: Learning to Speak via Interaction
ABSTRACT: One of the long-term goals of artificial intelligence is to build an agent
that can communicate intelligently with human in natural language. Most
existing work on natural language learning relies heavily on training over a
pre-collected dataset with annotated labels, leading to an agent that
essentially captures the statistics of the fixed external training data. As the
training data is essentially a static snapshot representation of the knowledge
from the annotator, the agent trained this way is limited in adaptiveness and
generalization of its behavior. Moreover, this is very different from the
language learning process of humans, where language is acquired during
communication by taking speaking action and learning from the consequences of
speaking action in an interactive manner. This paper presents an interactive
setting for grounded natural language learning, where an agent learns natural
language by interacting with a teacher and learning from feedback, thus
learning and improving language skills while taking part in the conversation.
To achieve this goal, we propose a model which incorporates both imitation and
reinforcement by leveraging jointly sentence and reward feedbacks from the
teacher. Experiments are conducted to validate the effectiveness of the
proposed approach.
| no_new_dataset | 0.946101 |
1705.09920 | Ali Nassif | Mohammad Azzeh, Ali Bou Nassif | Analyzing the Relationship between Project Productivity and Environment
Factors in the Use Case Points Method | Journal of Software: Evolution and Process, 2017 | null | 10.1002/smr.1882 | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Project productivity is a key factor for producing effort estimates from Use
Case Points (UCP), especially when the historical dataset is absent. The first
versions of UCP effort estimation models used a fixed number or very limited
numbers of productivity ratios for all new projects. These approaches have not
been well examined over a large number of projects so the validity of these
studies was a matter for criticism. The newly available large software datasets
allow us to perform further research on the usefulness of productivity for
effort estimation of software development. Specifically, we studied the
relationship between project productivity and UCP environmental factors, as
they have a significant impact on the amount of productivity needed for a
software project. Therefore, we designed four studies, using various
classification and regression methods, to examine the usefulness of that
relationship and its impact on UCP effort estimation. The results we obtained
are encouraging and show potential improvement in effort estimation.
Furthermore, the efficiency of that relationship is better over a dataset that
comes from industry because of the quality of data collection. Our comment on
the findings is that it is better to exclude environmental factors from
calculating UCP and make them available only for computing productivity. The
study also encourages project managers to understand how to better assess the
environmental factors as they do have a significant impact on productivity
| [
{
"version": "v1",
"created": "Sun, 28 May 2017 09:44:18 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Azzeh",
"Mohammad",
""
],
[
"Nassif",
"Ali Bou",
""
]
] | TITLE: Analyzing the Relationship between Project Productivity and Environment
Factors in the Use Case Points Method
ABSTRACT: Project productivity is a key factor for producing effort estimates from Use
Case Points (UCP), especially when the historical dataset is absent. The first
versions of UCP effort estimation models used a fixed number or very limited
numbers of productivity ratios for all new projects. These approaches have not
been well examined over a large number of projects so the validity of these
studies was a matter for criticism. The newly available large software datasets
allow us to perform further research on the usefulness of productivity for
effort estimation of software development. Specifically, we studied the
relationship between project productivity and UCP environmental factors, as
they have a significant impact on the amount of productivity needed for a
software project. Therefore, we designed four studies, using various
classification and regression methods, to examine the usefulness of that
relationship and its impact on UCP effort estimation. The results we obtained
are encouraging and show potential improvement in effort estimation.
Furthermore, the efficiency of that relationship is better over a dataset that
comes from industry because of the quality of data collection. Our comment on
the findings is that it is better to exclude environmental factors from
calculating UCP and make them available only for computing productivity. The
study also encourages project managers to understand how to better assess the
environmental factors as they do have a significant impact on productivity
| no_new_dataset | 0.929632 |
1705.09975 | Nazli Farajidavar | Nazli Farajidavar, Sefki Kolozali and Payam Barnaghi | A Deep Multi-View Learning Framework for City Event Extraction from
Twitter Data Streams | null | null | null | null | cs.SI cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cities have been a thriving place for citizens over the centuries due to
their complex infrastructure. The emergence of the Cyber-Physical-Social
Systems (CPSS) and context-aware technologies boost a growing interest in
analysing, extracting and eventually understanding city events which
subsequently can be utilised to leverage the citizen observations of their
cities. In this paper, we investigate the feasibility of using Twitter textual
streams for extracting city events. We propose a hierarchical multi-view deep
learning approach to contextualise citizen observations of various city systems
and services. Our goal has been to build a flexible architecture that can learn
representations useful for tasks, thus avoiding excessive task-specific feature
engineering. We apply our approach on a real-world dataset consisting of event
reports and tweets of over four months from San Francisco Bay Area dataset and
additional datasets collected from London. The results of our evaluations show
that our proposed solution outperforms the existing models and can be used for
extracting city related events with an averaged accuracy of 81% over all
classes. To further evaluate the impact of our Twitter event extraction model,
we have used two sources of authorised reports through collecting road traffic
disruptions data from Transport for London API, and parsing the Time Out London
website for sociocultural events. The analysis showed that 49.5% of the Twitter
traffic comments are reported approximately five hours prior to the authorities
official records. Moreover, we discovered that amongst the scheduled
sociocultural event topics; tweets reporting transportation, cultural and
social events are 31.75% more likely to influence the distribution of the
Twitter comments than sport, weather and crime topics.
| [
{
"version": "v1",
"created": "Sun, 28 May 2017 18:22:15 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Farajidavar",
"Nazli",
""
],
[
"Kolozali",
"Sefki",
""
],
[
"Barnaghi",
"Payam",
""
]
] | TITLE: A Deep Multi-View Learning Framework for City Event Extraction from
Twitter Data Streams
ABSTRACT: Cities have been a thriving place for citizens over the centuries due to
their complex infrastructure. The emergence of the Cyber-Physical-Social
Systems (CPSS) and context-aware technologies boost a growing interest in
analysing, extracting and eventually understanding city events which
subsequently can be utilised to leverage the citizen observations of their
cities. In this paper, we investigate the feasibility of using Twitter textual
streams for extracting city events. We propose a hierarchical multi-view deep
learning approach to contextualise citizen observations of various city systems
and services. Our goal has been to build a flexible architecture that can learn
representations useful for tasks, thus avoiding excessive task-specific feature
engineering. We apply our approach on a real-world dataset consisting of event
reports and tweets of over four months from San Francisco Bay Area dataset and
additional datasets collected from London. The results of our evaluations show
that our proposed solution outperforms the existing models and can be used for
extracting city related events with an averaged accuracy of 81% over all
classes. To further evaluate the impact of our Twitter event extraction model,
we have used two sources of authorised reports through collecting road traffic
disruptions data from Transport for London API, and parsing the Time Out London
website for sociocultural events. The analysis showed that 49.5% of the Twitter
traffic comments are reported approximately five hours prior to the authorities
official records. Moreover, we discovered that amongst the scheduled
sociocultural event topics; tweets reporting transportation, cultural and
social events are 31.75% more likely to influence the distribution of the
Twitter comments than sport, weather and crime topics.
| no_new_dataset | 0.875521 |
1705.10034 | Xiaopeng Zhang | Xiaopeng Zhang, Hongkai Xiong, Weiyao Lin, Qi Tian | Ensemble of Part Detectors for Simultaneous Classification and
Localization | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Part-based representation has been proven to be effective for a variety of
visual applications. However, automatic discovery of discriminative parts
without object/part-level annotations is challenging. This paper proposes a
discriminative mid-level representation paradigm based on the responses of a
collection of part detectors, which only requires the image-level labels.
Towards this goal, we first develop a detector-based spectral clustering method
to mine the representative and discriminative mid-level patterns for detector
initialization. The advantage of the proposed pattern mining technology is that
the distance metric based on detectors only focuses on discriminative details,
and a set of such grouped detectors offer an effective way for consistent
pattern mining. Relying on the discovered patterns, we further formulate the
detector learning process as a confidence-loss sparse Multiple Instance
Learning (cls-MIL) task, which considers the diversity of the positive samples,
while avoid drifting away the well localized ones by assigning a confidence
value to each positive sample. The responses of the learned detectors can form
an effective mid-level image representation for both image classification and
object localization. Experiments conducted on benchmark datasets demonstrate
the superiority of our method over existing approaches.
| [
{
"version": "v1",
"created": "Mon, 29 May 2017 04:04:08 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Zhang",
"Xiaopeng",
""
],
[
"Xiong",
"Hongkai",
""
],
[
"Lin",
"Weiyao",
""
],
[
"Tian",
"Qi",
""
]
] | TITLE: Ensemble of Part Detectors for Simultaneous Classification and
Localization
ABSTRACT: Part-based representation has been proven to be effective for a variety of
visual applications. However, automatic discovery of discriminative parts
without object/part-level annotations is challenging. This paper proposes a
discriminative mid-level representation paradigm based on the responses of a
collection of part detectors, which only requires the image-level labels.
Towards this goal, we first develop a detector-based spectral clustering method
to mine the representative and discriminative mid-level patterns for detector
initialization. The advantage of the proposed pattern mining technology is that
the distance metric based on detectors only focuses on discriminative details,
and a set of such grouped detectors offer an effective way for consistent
pattern mining. Relying on the discovered patterns, we further formulate the
detector learning process as a confidence-loss sparse Multiple Instance
Learning (cls-MIL) task, which considers the diversity of the positive samples,
while avoid drifting away the well localized ones by assigning a confidence
value to each positive sample. The responses of the learned detectors can form
an effective mid-level image representation for both image classification and
object localization. Experiments conducted on benchmark datasets demonstrate
the superiority of our method over existing approaches.
| no_new_dataset | 0.951233 |
1705.10130 | Murtadha AL-Sharuee | Murtadha Talib AL-Sharuee, Fei Liu, Mahardhika Pratama | An Automatic Contextual Analysis and Clustering Classifiers Ensemble
approach to Sentiment Analysis | This article is submitted to a journal | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Products reviews are one of the major resources to determine the public
sentiment. The existing literature on reviews sentiment analysis mainly
utilizes supervised paradigm, which needs labeled data to be trained on and
suffers from domain-dependency. This article addresses these issues by
describes a completely automatic approach for sentiment analysis based on
unsupervised ensemble learning. The method consists of two phases. The first
phase is contextual analysis, which has five processes, namely (1) data
preparation; (2) spelling correction; (3) intensifier handling; (4) negation
handling and (5) contrast handling. The second phase comprises the unsupervised
learning approach, which is an ensemble of clustering classifiers using a
majority voting mechanism with different weight schemes. The base classifier of
the ensemble method is a modified k-means algorithm. The base classifier is
modified by extracting initial centroids from the feature set via using
SentWordNet (SWN). We also introduce new sentiment analysis problems of
Australian airlines and home builders which offer potential benchmark problems
in the sentiment analysis field. Our experiments on datasets from different
domains show that contextual analysis and the ensemble phases improve the
clustering performance in term of accuracy, stability and generalization
ability.
| [
{
"version": "v1",
"created": "Mon, 29 May 2017 11:37:58 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"AL-Sharuee",
"Murtadha Talib",
""
],
[
"Liu",
"Fei",
""
],
[
"Pratama",
"Mahardhika",
""
]
] | TITLE: An Automatic Contextual Analysis and Clustering Classifiers Ensemble
approach to Sentiment Analysis
ABSTRACT: Products reviews are one of the major resources to determine the public
sentiment. The existing literature on reviews sentiment analysis mainly
utilizes supervised paradigm, which needs labeled data to be trained on and
suffers from domain-dependency. This article addresses these issues by
describes a completely automatic approach for sentiment analysis based on
unsupervised ensemble learning. The method consists of two phases. The first
phase is contextual analysis, which has five processes, namely (1) data
preparation; (2) spelling correction; (3) intensifier handling; (4) negation
handling and (5) contrast handling. The second phase comprises the unsupervised
learning approach, which is an ensemble of clustering classifiers using a
majority voting mechanism with different weight schemes. The base classifier of
the ensemble method is a modified k-means algorithm. The base classifier is
modified by extracting initial centroids from the feature set via using
SentWordNet (SWN). We also introduce new sentiment analysis problems of
Australian airlines and home builders which offer potential benchmark problems
in the sentiment analysis field. Our experiments on datasets from different
domains show that contextual analysis and the ensemble phases improve the
clustering performance in term of accuracy, stability and generalization
ability.
| no_new_dataset | 0.945751 |
1705.10194 | Feng Nan | Feng Nan, Venkatesh Saligrama | Adaptive Classification for Prediction Under a Budget | arXiv admin note: substantial text overlap with arXiv:1704.07505 | null | null | null | stat.ML cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel adaptive approximation approach for test-time
resource-constrained prediction. Given an input instance at test-time, a gating
function identifies a prediction model for the input among a collection of
models. Our objective is to minimize overall average cost without sacrificing
accuracy. We learn gating and prediction models on fully labeled training data
by means of a bottom-up strategy. Our novel bottom-up method first trains a
high-accuracy complex model. Then a low-complexity gating and prediction model
are subsequently learned to adaptively approximate the high-accuracy model in
regions where low-cost models are capable of making highly accurate
predictions. We pose an empirical loss minimization problem with cost
constraints to jointly train gating and prediction models. On a number of
benchmark datasets our method outperforms state-of-the-art achieving higher
accuracy for the same cost.
| [
{
"version": "v1",
"created": "Fri, 26 May 2017 12:28:42 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Nan",
"Feng",
""
],
[
"Saligrama",
"Venkatesh",
""
]
] | TITLE: Adaptive Classification for Prediction Under a Budget
ABSTRACT: We propose a novel adaptive approximation approach for test-time
resource-constrained prediction. Given an input instance at test-time, a gating
function identifies a prediction model for the input among a collection of
models. Our objective is to minimize overall average cost without sacrificing
accuracy. We learn gating and prediction models on fully labeled training data
by means of a bottom-up strategy. Our novel bottom-up method first trains a
high-accuracy complex model. Then a low-complexity gating and prediction model
are subsequently learned to adaptively approximate the high-accuracy model in
regions where low-cost models are capable of making highly accurate
predictions. We pose an empirical loss minimization problem with cost
constraints to jointly train gating and prediction models. On a number of
benchmark datasets our method outperforms state-of-the-art achieving higher
accuracy for the same cost.
| no_new_dataset | 0.941815 |
1705.10209 | Micha{\l} Zapotoczny | Micha{\l} Zapotoczny, Pawe{\l} Rychlikowski, and Jan Chorowski | On Multilingual Training of Neural Dependency Parsers | preprint accepted into the TSD2017 | null | null | null | cs.CL cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that a recently proposed neural dependency parser can be improved by
joint training on multiple languages from the same family. The parser is
implemented as a deep neural network whose only input is orthographic
representations of words. In order to successfully parse, the network has to
discover how linguistically relevant concepts can be inferred from word
spellings. We analyze the representations of characters and words that are
learned by the network to establish which properties of languages were
accounted for. In particular we show that the parser has approximately learned
to associate Latin characters with their Cyrillic counterparts and that it can
group Polish and Russian words that have a similar grammatical function.
Finally, we evaluate the parser on selected languages from the Universal
Dependencies dataset and show that it is competitive with other recently
proposed state-of-the art methods, while having a simple structure.
| [
{
"version": "v1",
"created": "Mon, 29 May 2017 14:24:08 GMT"
}
] | 2017-05-30T00:00:00 | [
[
"Zapotoczny",
"Michał",
""
],
[
"Rychlikowski",
"Paweł",
""
],
[
"Chorowski",
"Jan",
""
]
] | TITLE: On Multilingual Training of Neural Dependency Parsers
ABSTRACT: We show that a recently proposed neural dependency parser can be improved by
joint training on multiple languages from the same family. The parser is
implemented as a deep neural network whose only input is orthographic
representations of words. In order to successfully parse, the network has to
discover how linguistically relevant concepts can be inferred from word
spellings. We analyze the representations of characters and words that are
learned by the network to establish which properties of languages were
accounted for. In particular we show that the parser has approximately learned
to associate Latin characters with their Cyrillic counterparts and that it can
group Polish and Russian words that have a similar grammatical function.
Finally, we evaluate the parser on selected languages from the Universal
Dependencies dataset and show that it is competitive with other recently
proposed state-of-the art methods, while having a simple structure.
| no_new_dataset | 0.940463 |
1511.07111 | Eric Tzeng | Eric Tzeng, Coline Devin, Judy Hoffman, Chelsea Finn, Pieter Abbeel,
Sergey Levine, Kate Saenko, Trevor Darrell | Adapting Deep Visuomotor Representations with Weak Pairwise Constraints | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Real-world robotics problems often occur in domains that differ significantly
from the robot's prior training environment. For many robotic control tasks,
real world experience is expensive to obtain, but data is easy to collect in
either an instrumented environment or in simulation. We propose a novel domain
adaptation approach for robot perception that adapts visual representations
learned on a large easy-to-obtain source dataset (e.g. synthetic images) to a
target real-world domain, without requiring expensive manual data annotation of
real world data before policy search. Supervised domain adaptation methods
minimize cross-domain differences using pairs of aligned images that contain
the same object or scene in both the source and target domains, thus learning a
domain-invariant representation. However, they require manual alignment of such
image pairs. Fully unsupervised adaptation methods rely on minimizing the
discrepancy between the feature distributions across domains. We propose a
novel, more powerful combination of both distribution and pairwise image
alignment, and remove the requirement for expensive annotation by using weakly
aligned pairs of images in the source and target domains. Focusing on adapting
from simulation to real world data using a PR2 robot, we evaluate our approach
on a manipulation task and show that by using weakly paired images, our method
compensates for domain shift more effectively than previous techniques,
enabling better robot performance in the real world.
| [
{
"version": "v1",
"created": "Mon, 23 Nov 2015 05:07:15 GMT"
},
{
"version": "v2",
"created": "Mon, 11 Jan 2016 19:55:50 GMT"
},
{
"version": "v3",
"created": "Tue, 12 Apr 2016 22:05:27 GMT"
},
{
"version": "v4",
"created": "Mon, 21 Nov 2016 21:37:58 GMT"
},
{
"version": "v5",
"created": "Thu, 25 May 2017 21:51:55 GMT"
}
] | 2017-05-29T00:00:00 | [
[
"Tzeng",
"Eric",
""
],
[
"Devin",
"Coline",
""
],
[
"Hoffman",
"Judy",
""
],
[
"Finn",
"Chelsea",
""
],
[
"Abbeel",
"Pieter",
""
],
[
"Levine",
"Sergey",
""
],
[
"Saenko",
"Kate",
""
],
[
"Darrell",
"Trevor",
""
]
] | TITLE: Adapting Deep Visuomotor Representations with Weak Pairwise Constraints
ABSTRACT: Real-world robotics problems often occur in domains that differ significantly
from the robot's prior training environment. For many robotic control tasks,
real world experience is expensive to obtain, but data is easy to collect in
either an instrumented environment or in simulation. We propose a novel domain
adaptation approach for robot perception that adapts visual representations
learned on a large easy-to-obtain source dataset (e.g. synthetic images) to a
target real-world domain, without requiring expensive manual data annotation of
real world data before policy search. Supervised domain adaptation methods
minimize cross-domain differences using pairs of aligned images that contain
the same object or scene in both the source and target domains, thus learning a
domain-invariant representation. However, they require manual alignment of such
image pairs. Fully unsupervised adaptation methods rely on minimizing the
discrepancy between the feature distributions across domains. We propose a
novel, more powerful combination of both distribution and pairwise image
alignment, and remove the requirement for expensive annotation by using weakly
aligned pairs of images in the source and target domains. Focusing on adapting
from simulation to real world data using a PR2 robot, we evaluate our approach
on a manipulation task and show that by using weakly paired images, our method
compensates for domain shift more effectively than previous techniques,
enabling better robot performance in the real world.
| no_new_dataset | 0.95452 |
1610.02891 | Kaixiang Mo | Kaixiang Mo, Shuangyin Li, Yu Zhang, Jiajun Li, Qiang Yang | Personalizing a Dialogue System with Transfer Reinforcement Learning | null | null | null | null | cs.AI cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is difficult to train a personalized task-oriented dialogue system because
the data collected from each individual is often insufficient. Personalized
dialogue systems trained on a small dataset can overfit and make it difficult
to adapt to different user needs. One way to solve this problem is to consider
a collection of multiple users' data as a source domain and an individual
user's data as a target domain, and to perform a transfer learning from the
source to the target domain. By following this idea, we propose
"PETAL"(PErsonalized Task-oriented diALogue), a transfer-learning framework
based on POMDP to learn a personalized dialogue system. The system first learns
common dialogue knowledge from the source domain and then adapts this knowledge
to the target user. This framework can avoid the negative transfer problem by
considering differences between source and target users. The policy in the
personalized POMDP can learn to choose different actions appropriately for
different users. Experimental results on a real-world coffee-shopping data and
simulation data show that our personalized dialogue system can choose different
optimal actions for different users, and thus effectively improve the dialogue
quality under the personalized setting.
| [
{
"version": "v1",
"created": "Mon, 10 Oct 2016 12:51:05 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Oct 2016 14:08:42 GMT"
},
{
"version": "v3",
"created": "Fri, 26 May 2017 14:05:07 GMT"
}
] | 2017-05-29T00:00:00 | [
[
"Mo",
"Kaixiang",
""
],
[
"Li",
"Shuangyin",
""
],
[
"Zhang",
"Yu",
""
],
[
"Li",
"Jiajun",
""
],
[
"Yang",
"Qiang",
""
]
] | TITLE: Personalizing a Dialogue System with Transfer Reinforcement Learning
ABSTRACT: It is difficult to train a personalized task-oriented dialogue system because
the data collected from each individual is often insufficient. Personalized
dialogue systems trained on a small dataset can overfit and make it difficult
to adapt to different user needs. One way to solve this problem is to consider
a collection of multiple users' data as a source domain and an individual
user's data as a target domain, and to perform a transfer learning from the
source to the target domain. By following this idea, we propose
"PETAL"(PErsonalized Task-oriented diALogue), a transfer-learning framework
based on POMDP to learn a personalized dialogue system. The system first learns
common dialogue knowledge from the source domain and then adapts this knowledge
to the target user. This framework can avoid the negative transfer problem by
considering differences between source and target users. The policy in the
personalized POMDP can learn to choose different actions appropriately for
different users. Experimental results on a real-world coffee-shopping data and
simulation data show that our personalized dialogue system can choose different
optimal actions for different users, and thus effectively improve the dialogue
quality under the personalized setting.
| no_new_dataset | 0.946892 |
1705.07844 | Paul Guerrero | Paul Guerrero, Holger Winnem\"oller, Wilmot Li, Niloy J. Mitra | DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable
Channels | 12 pages | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the context of scene understanding, a variety of methods exists to
estimate different information channels from mono or stereo images, including
disparity, depth, and normals. Although several advances have been reported in
the recent years for these tasks, the estimated information is often imprecise
particularly near depth discontinuities or creases. Studies have however shown
that precisely such depth edges carry critical cues for the perception of
shape, and play important roles in tasks like depth-based segmentation or
foreground selection. Unfortunately, the currently extracted channels often
carry conflicting signals, making it difficult for subsequent applications to
effectively use them. In this paper, we focus on the problem of obtaining
high-precision depth edges (i.e., depth contours and creases) by jointly
analyzing such unreliable information channels. We propose DepthCut, a
data-driven fusion of the channels using a convolutional neural network trained
on a large dataset with known depth. The resulting depth edges can be used for
segmentation, decomposing a scene into depth layers with relatively flat depth,
or improving the accuracy of the depth estimate near depth edges by
constraining its gradients to agree with these edges. Quantitatively, we
compare against 15 variants of baselines and demonstrate that our depth edges
result in an improved segmentation performance and an improved depth estimate
near depth edges compared to data-agnostic channel fusion. Qualitatively, we
demonstrate that the depth edges result in superior segmentation and depth
orderings.
| [
{
"version": "v1",
"created": "Mon, 22 May 2017 16:48:15 GMT"
},
{
"version": "v2",
"created": "Fri, 26 May 2017 14:21:54 GMT"
}
] | 2017-05-29T00:00:00 | [
[
"Guerrero",
"Paul",
""
],
[
"Winnemöller",
"Holger",
""
],
[
"Li",
"Wilmot",
""
],
[
"Mitra",
"Niloy J.",
""
]
] | TITLE: DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable
Channels
ABSTRACT: In the context of scene understanding, a variety of methods exists to
estimate different information channels from mono or stereo images, including
disparity, depth, and normals. Although several advances have been reported in
the recent years for these tasks, the estimated information is often imprecise
particularly near depth discontinuities or creases. Studies have however shown
that precisely such depth edges carry critical cues for the perception of
shape, and play important roles in tasks like depth-based segmentation or
foreground selection. Unfortunately, the currently extracted channels often
carry conflicting signals, making it difficult for subsequent applications to
effectively use them. In this paper, we focus on the problem of obtaining
high-precision depth edges (i.e., depth contours and creases) by jointly
analyzing such unreliable information channels. We propose DepthCut, a
data-driven fusion of the channels using a convolutional neural network trained
on a large dataset with known depth. The resulting depth edges can be used for
segmentation, decomposing a scene into depth layers with relatively flat depth,
or improving the accuracy of the depth estimate near depth edges by
constraining its gradients to agree with these edges. Quantitatively, we
compare against 15 variants of baselines and demonstrate that our depth edges
result in an improved segmentation performance and an improved depth estimate
near depth edges compared to data-agnostic channel fusion. Qualitatively, we
demonstrate that the depth edges result in superior segmentation and depth
orderings.
| no_new_dataset | 0.954563 |
1705.08039 | Maximilian Nickel | Maximilian Nickel, Douwe Kiela | Poincar\'e Embeddings for Learning Hierarchical Representations | null | null | null | null | cs.AI cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Representation learning has become an invaluable approach for learning from
symbolic data such as text and graphs. However, while complex symbolic datasets
often exhibit a latent hierarchical structure, state-of-the-art methods
typically learn embeddings in Euclidean vector spaces, which do not account for
this property. For this purpose, we introduce a new approach for learning
hierarchical representations of symbolic data by embedding them into hyperbolic
space -- or more precisely into an n-dimensional Poincar\'e ball. Due to the
underlying hyperbolic geometry, this allows us to learn parsimonious
representations of symbolic data by simultaneously capturing hierarchy and
similarity. We introduce an efficient algorithm to learn the embeddings based
on Riemannian optimization and show experimentally that Poincar\'e embeddings
outperform Euclidean embeddings significantly on data with latent hierarchies,
both in terms of representation capacity and in terms of generalization
ability.
| [
{
"version": "v1",
"created": "Mon, 22 May 2017 23:14:36 GMT"
},
{
"version": "v2",
"created": "Fri, 26 May 2017 17:40:55 GMT"
}
] | 2017-05-29T00:00:00 | [
[
"Nickel",
"Maximilian",
""
],
[
"Kiela",
"Douwe",
""
]
] | TITLE: Poincar\'e Embeddings for Learning Hierarchical Representations
ABSTRACT: Representation learning has become an invaluable approach for learning from
symbolic data such as text and graphs. However, while complex symbolic datasets
often exhibit a latent hierarchical structure, state-of-the-art methods
typically learn embeddings in Euclidean vector spaces, which do not account for
this property. For this purpose, we introduce a new approach for learning
hierarchical representations of symbolic data by embedding them into hyperbolic
space -- or more precisely into an n-dimensional Poincar\'e ball. Due to the
underlying hyperbolic geometry, this allows us to learn parsimonious
representations of symbolic data by simultaneously capturing hierarchy and
similarity. We introduce an efficient algorithm to learn the embeddings based
on Riemannian optimization and show experimentally that Poincar\'e embeddings
outperform Euclidean embeddings significantly on data with latent hierarchies,
both in terms of representation capacity and in terms of generalization
ability.
| no_new_dataset | 0.948394 |
1705.09366 | Erik Saule | Erik Saule, Dinesh Panchananam, Alexander Hohl, Wenwu Tang, Eric
Delmelle | Parallel Space-Time Kernel Density Estimation | null | null | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The exponential growth of available data has increased the need for
interactive exploratory analysis. Dataset can no longer be understood through
manual crawling and simple statistics. In Geographical Information Systems
(GIS), the dataset is often composed of events localized in space and time; and
visualizing such a dataset involves building a map of where the events
occurred.
We focus in this paper on events that are localized among three dimensions
(latitude, longitude, and time), and on computing the first step of the
visualization pipeline, space-time kernel density estimation (STKDE), which is
most computationally expensive. Starting from a gold standard implementation,
we show how algorithm design and engineering, parallel decomposition, and
scheduling can be applied to bring near real-time computing to space-time
kernel density estimation. We validate our techniques on real world datasets
extracted from infectious disease, social media, and ornithology.
| [
{
"version": "v1",
"created": "Thu, 25 May 2017 21:16:37 GMT"
}
] | 2017-05-29T00:00:00 | [
[
"Saule",
"Erik",
""
],
[
"Panchananam",
"Dinesh",
""
],
[
"Hohl",
"Alexander",
""
],
[
"Tang",
"Wenwu",
""
],
[
"Delmelle",
"Eric",
""
]
] | TITLE: Parallel Space-Time Kernel Density Estimation
ABSTRACT: The exponential growth of available data has increased the need for
interactive exploratory analysis. Dataset can no longer be understood through
manual crawling and simple statistics. In Geographical Information Systems
(GIS), the dataset is often composed of events localized in space and time; and
visualizing such a dataset involves building a map of where the events
occurred.
We focus in this paper on events that are localized among three dimensions
(latitude, longitude, and time), and on computing the first step of the
visualization pipeline, space-time kernel density estimation (STKDE), which is
most computationally expensive. Starting from a gold standard implementation,
we show how algorithm design and engineering, parallel decomposition, and
scheduling can be applied to bring near real-time computing to space-time
kernel density estimation. We validate our techniques on real world datasets
extracted from infectious disease, social media, and ornithology.
| no_new_dataset | 0.947527 |
1705.09425 | Yao Qin | Yao Qin, Mengyang Feng, Huchuan Lu, Garrison W. Cottrell | Hierarchical Cellular Automata for Visual Saliency | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Saliency detection, finding the most important parts of an image, has become
increasingly popular in computer vision. In this paper, we introduce
Hierarchical Cellular Automata (HCA) -- a temporally evolving model to
intelligently detect salient objects. HCA consists of two main components:
Single-layer Cellular Automata (SCA) and Cuboid Cellular Automata (CCA). As an
unsupervised propagation mechanism, Single-layer Cellular Automata can exploit
the intrinsic relevance of similar regions through interactions with neighbors.
Low-level image features as well as high-level semantic information extracted
from deep neural networks are incorporated into the SCA to measure the
correlation between different image patches. With these hierarchical deep
features, an impact factor matrix and a coherence matrix are constructed to
balance the influences on each cell's next state. The saliency values of all
cells are iteratively updated according to a well-defined update rule.
Furthermore, we propose CCA to integrate multiple saliency maps generated by
SCA at different scales in a Bayesian framework. Therefore, single-layer
propagation and multi-layer integration are jointly modeled in our unified HCA.
Surprisingly, we find that the SCA can improve all existing methods that we
applied it to, resulting in a similar precision level regardless of the
original results. The CCA can act as an efficient pixel-wise aggregation
algorithm that can integrate state-of-the-art methods, resulting in even better
results. Extensive experiments on four challenging datasets demonstrate that
the proposed algorithm outperforms state-of-the-art conventional methods and is
competitive with deep learning based approaches.
| [
{
"version": "v1",
"created": "Fri, 26 May 2017 03:43:16 GMT"
}
] | 2017-05-29T00:00:00 | [
[
"Qin",
"Yao",
""
],
[
"Feng",
"Mengyang",
""
],
[
"Lu",
"Huchuan",
""
],
[
"Cottrell",
"Garrison W.",
""
]
] | TITLE: Hierarchical Cellular Automata for Visual Saliency
ABSTRACT: Saliency detection, finding the most important parts of an image, has become
increasingly popular in computer vision. In this paper, we introduce
Hierarchical Cellular Automata (HCA) -- a temporally evolving model to
intelligently detect salient objects. HCA consists of two main components:
Single-layer Cellular Automata (SCA) and Cuboid Cellular Automata (CCA). As an
unsupervised propagation mechanism, Single-layer Cellular Automata can exploit
the intrinsic relevance of similar regions through interactions with neighbors.
Low-level image features as well as high-level semantic information extracted
from deep neural networks are incorporated into the SCA to measure the
correlation between different image patches. With these hierarchical deep
features, an impact factor matrix and a coherence matrix are constructed to
balance the influences on each cell's next state. The saliency values of all
cells are iteratively updated according to a well-defined update rule.
Furthermore, we propose CCA to integrate multiple saliency maps generated by
SCA at different scales in a Bayesian framework. Therefore, single-layer
propagation and multi-layer integration are jointly modeled in our unified HCA.
Surprisingly, we find that the SCA can improve all existing methods that we
applied it to, resulting in a similar precision level regardless of the
original results. The CCA can act as an efficient pixel-wise aggregation
algorithm that can integrate state-of-the-art methods, resulting in even better
results. Extensive experiments on four challenging datasets demonstrate that
the proposed algorithm outperforms state-of-the-art conventional methods and is
competitive with deep learning based approaches.
| no_new_dataset | 0.94801 |
1705.09439 | Kosetsu Tsukuda | Kosetsu Tsukuda, Masataka Goto | Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation | Accepted by The 21st Pacific-Asia Conference on Knowledge Discovery
and Data Mining (PAKDD 2017) | null | null | null | cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Online music services are increasing in popularity. They enable us to analyze
people's music listening behavior based on play logs. Although it is known that
people listen to music based on topic (e.g., rock or jazz), we assume that when
a user is addicted to an artist, s/he chooses the artist's songs regardless of
topic. Based on this assumption, in this paper, we propose a probabilistic
model to analyze people's music listening behavior. Our main contributions are
three-fold. First, to the best of our knowledge, this is the first study
modeling music listening behavior by taking into account the influence of
addiction to artists. Second, by using real-world datasets of play logs, we
showed the effectiveness of our proposed model. Third, we carried out
qualitative experiments and showed that taking addiction into account enables
us to analyze music listening behavior from a new viewpoint in terms of how
people listen to music according to the time of day, how an artist's songs are
listened to by people, etc. We also discuss the possibility of applying the
analysis results to applications such as artist similarity computation and song
recommendation.
| [
{
"version": "v1",
"created": "Fri, 26 May 2017 05:54:20 GMT"
}
] | 2017-05-29T00:00:00 | [
[
"Tsukuda",
"Kosetsu",
""
],
[
"Goto",
"Masataka",
""
]
] | TITLE: Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation
ABSTRACT: Online music services are increasing in popularity. They enable us to analyze
people's music listening behavior based on play logs. Although it is known that
people listen to music based on topic (e.g., rock or jazz), we assume that when
a user is addicted to an artist, s/he chooses the artist's songs regardless of
topic. Based on this assumption, in this paper, we propose a probabilistic
model to analyze people's music listening behavior. Our main contributions are
three-fold. First, to the best of our knowledge, this is the first study
modeling music listening behavior by taking into account the influence of
addiction to artists. Second, by using real-world datasets of play logs, we
showed the effectiveness of our proposed model. Third, we carried out
qualitative experiments and showed that taking addiction into account enables
us to analyze music listening behavior from a new viewpoint in terms of how
people listen to music according to the time of day, how an artist's songs are
listened to by people, etc. We also discuss the possibility of applying the
analysis results to applications such as artist similarity computation and song
recommendation.
| no_new_dataset | 0.953144 |
1705.09467 | Yichao Yan | Yichao Yan, Bingbing Ni, Xiaokang Yang | Predicting Human Interaction via Relative Attention Model | To appear in IJCAI 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predicting human interaction is challenging as the on-going activity has to
be inferred based on a partially observed video. Essentially, a good algorithm
should effectively model the mutual influence between the two interacting
subjects. Also, only a small region in the scene is discriminative for
identifying the on-going interaction. In this work, we propose a relative
attention model to explicitly address these difficulties. Built on a
tri-coupled deep recurrent structure representing both interacting subjects and
global interaction status, the proposed network collects spatio-temporal
information from each subject, rectified with global interaction information,
yielding effective interaction representation. Moreover, the proposed network
also unifies an attention module to assign higher importance to the regions
which are relevant to the on-going action. Extensive experiments have been
conducted on two public datasets, and the results demonstrate that the proposed
relative attention network successfully predicts informative regions between
interacting subjects, which in turn yields superior human interaction
prediction accuracy.
| [
{
"version": "v1",
"created": "Fri, 26 May 2017 08:04:24 GMT"
}
] | 2017-05-29T00:00:00 | [
[
"Yan",
"Yichao",
""
],
[
"Ni",
"Bingbing",
""
],
[
"Yang",
"Xiaokang",
""
]
] | TITLE: Predicting Human Interaction via Relative Attention Model
ABSTRACT: Predicting human interaction is challenging as the on-going activity has to
be inferred based on a partially observed video. Essentially, a good algorithm
should effectively model the mutual influence between the two interacting
subjects. Also, only a small region in the scene is discriminative for
identifying the on-going interaction. In this work, we propose a relative
attention model to explicitly address these difficulties. Built on a
tri-coupled deep recurrent structure representing both interacting subjects and
global interaction status, the proposed network collects spatio-temporal
information from each subject, rectified with global interaction information,
yielding effective interaction representation. Moreover, the proposed network
also unifies an attention module to assign higher importance to the regions
which are relevant to the on-going action. Extensive experiments have been
conducted on two public datasets, and the results demonstrate that the proposed
relative attention network successfully predicts informative regions between
interacting subjects, which in turn yields superior human interaction
prediction accuracy.
| no_new_dataset | 0.947527 |
1705.09474 | Donghui Wang | Yanan Li, Donghui Wang | Zero-Shot Learning with Generative Latent Prototype Model | This work was completed in Oct, 2016 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Zero-shot learning, which studies the problem of object classification for
categories for which we have no training examples, is gaining increasing
attention from community. Most existing ZSL methods exploit deterministic
transfer learning via an in-between semantic embedding space. In this paper, we
try to attack this problem from a generative probabilistic modelling
perspective. We assume for any category, the observed representation, e.g.
images or texts, is developed from a unique prototype in a latent space, in
which the semantic relationship among prototypes is encoded via linear
reconstruction. Taking advantage of this assumption, virtual instances of
unseen classes can be generated from the corresponding prototype, giving rise
to a novel ZSL model which can alleviate the domain shift problem existing in
the way of direct transfer learning. Extensive experiments on three benchmark
datasets show our proposed model can achieve state-of-the-art results.
| [
{
"version": "v1",
"created": "Fri, 26 May 2017 08:22:13 GMT"
}
] | 2017-05-29T00:00:00 | [
[
"Li",
"Yanan",
""
],
[
"Wang",
"Donghui",
""
]
] | TITLE: Zero-Shot Learning with Generative Latent Prototype Model
ABSTRACT: Zero-shot learning, which studies the problem of object classification for
categories for which we have no training examples, is gaining increasing
attention from community. Most existing ZSL methods exploit deterministic
transfer learning via an in-between semantic embedding space. In this paper, we
try to attack this problem from a generative probabilistic modelling
perspective. We assume for any category, the observed representation, e.g.
images or texts, is developed from a unique prototype in a latent space, in
which the semantic relationship among prototypes is encoded via linear
reconstruction. Taking advantage of this assumption, virtual instances of
unseen classes can be generated from the corresponding prototype, giving rise
to a novel ZSL model which can alleviate the domain shift problem existing in
the way of direct transfer learning. Extensive experiments on three benchmark
datasets show our proposed model can achieve state-of-the-art results.
| no_new_dataset | 0.949529 |
1705.09476 | Donghui Wang | Yanan Li, Donghui Wang | Learning Robust Features with Incremental Auto-Encoders | This work was completed in Feb, 2015 | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatically learning features, especially robust features, has attracted
much attention in the machine learning community. In this paper, we propose a
new method to learn non-linear robust features by taking advantage of the data
manifold structure. We first follow the commonly used trick of the trade, that
is learning robust features with artificially corrupted data, which are
training samples with manually injected noise. Following the idea of the
auto-encoder, we first assume features should contain much information to well
reconstruct the input from its corrupted copies. However, merely reconstructing
clean input from its noisy copies could make data manifold in the feature space
noisy. To address this problem, we propose a new method, called Incremental
Auto-Encoders, to iteratively denoise the extracted features. We assume the
noisy manifold structure is caused by a diffusion process. Consequently, we
reverse this specific diffusion process to further contract this noisy
manifold, which results in an incremental optimization of model parameters .
Furthermore, we show these learned non-linear features can be stacked into a
hierarchy of features. Experimental results on real-world datasets demonstrate
the proposed method can achieve better classification performances.
| [
{
"version": "v1",
"created": "Fri, 26 May 2017 08:30:41 GMT"
}
] | 2017-05-29T00:00:00 | [
[
"Li",
"Yanan",
""
],
[
"Wang",
"Donghui",
""
]
] | TITLE: Learning Robust Features with Incremental Auto-Encoders
ABSTRACT: Automatically learning features, especially robust features, has attracted
much attention in the machine learning community. In this paper, we propose a
new method to learn non-linear robust features by taking advantage of the data
manifold structure. We first follow the commonly used trick of the trade, that
is learning robust features with artificially corrupted data, which are
training samples with manually injected noise. Following the idea of the
auto-encoder, we first assume features should contain much information to well
reconstruct the input from its corrupted copies. However, merely reconstructing
clean input from its noisy copies could make data manifold in the feature space
noisy. To address this problem, we propose a new method, called Incremental
Auto-Encoders, to iteratively denoise the extracted features. We assume the
noisy manifold structure is caused by a diffusion process. Consequently, we
reverse this specific diffusion process to further contract this noisy
manifold, which results in an incremental optimization of model parameters .
Furthermore, we show these learned non-linear features can be stacked into a
hierarchy of features. Experimental results on real-world datasets demonstrate
the proposed method can achieve better classification performances.
| no_new_dataset | 0.944228 |
1705.09602 | Elena Burceanu | Elena Burceanu and Marius Leordeanu | Learning a Robust Society of Tracking Parts | 9.5 pages of main content, 2.5 of bibliography, 2 pages of appendix,
3 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Object tracking is an essential task in computer vision that has been studied
since the early days of the field. Being able to follow objects that undergo
different transformations in the video sequence, including changes in scale,
illumination, shape and occlusions, makes the problem extremely difficult. One
of the real challenges is to keep track of the changes in objects appearance
and not drift towards the background clutter. Different from previous
approaches, we obtain robustness against background with a tracker model that
is composed of many different parts. They are classifiers that respond at
different scales and locations. The tracker system functions as a society of
parts, each having its own role and level of credibility. Reliable classifiers
decide the tracker's next move, while newcomers are first monitored before
gaining the necessary level of reliability to participate in the decision
process. Some parts that loose their consistency are rejected, while others
that show consistency for a sufficiently long time are promoted to permanent
roles. The tracker system, as a whole, could also go through different phases,
from the usual, normal functioning to states of weak agreement and even crisis.
The tracker system has different governing rules in each state. What truly
distinguishes our work from others is not necessarily the strength of
individual tracking parts, but the way in which they work together and build a
strong and robust organization. We also propose an efficient way to learn
simultaneously many tracking parts, with a single closed-form formulation. We
obtain a fast and robust tracker with state of the art performance on the
challenging OTB50 dataset.
| [
{
"version": "v1",
"created": "Fri, 26 May 2017 14:51:43 GMT"
}
] | 2017-05-29T00:00:00 | [
[
"Burceanu",
"Elena",
""
],
[
"Leordeanu",
"Marius",
""
]
] | TITLE: Learning a Robust Society of Tracking Parts
ABSTRACT: Object tracking is an essential task in computer vision that has been studied
since the early days of the field. Being able to follow objects that undergo
different transformations in the video sequence, including changes in scale,
illumination, shape and occlusions, makes the problem extremely difficult. One
of the real challenges is to keep track of the changes in objects appearance
and not drift towards the background clutter. Different from previous
approaches, we obtain robustness against background with a tracker model that
is composed of many different parts. They are classifiers that respond at
different scales and locations. The tracker system functions as a society of
parts, each having its own role and level of credibility. Reliable classifiers
decide the tracker's next move, while newcomers are first monitored before
gaining the necessary level of reliability to participate in the decision
process. Some parts that loose their consistency are rejected, while others
that show consistency for a sufficiently long time are promoted to permanent
roles. The tracker system, as a whole, could also go through different phases,
from the usual, normal functioning to states of weak agreement and even crisis.
The tracker system has different governing rules in each state. What truly
distinguishes our work from others is not necessarily the strength of
individual tracking parts, but the way in which they work together and build a
strong and robust organization. We also propose an efficient way to learn
simultaneously many tracking parts, with a single closed-form formulation. We
obtain a fast and robust tracker with state of the art performance on the
challenging OTB50 dataset.
| no_new_dataset | 0.940024 |
1610.02616 | Zecheng Xie | Zecheng Xie, Zenghui Sun, Lianwen Jin, Hao Ni, Terry Lyons | Learning Spatial-Semantic Context with Fully Convolutional Recurrent
Network for Online Handwritten Chinese Text Recognition | 14 pages, 9 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Online handwritten Chinese text recognition (OHCTR) is a challenging problem
as it involves a large-scale character set, ambiguous segmentation, and
variable-length input sequences. In this paper, we exploit the outstanding
capability of path signature to translate online pen-tip trajectories into
informative signature feature maps using a sliding window-based method,
successfully capturing the analytic and geometric properties of pen strokes
with strong local invariance and robustness. A multi-spatial-context fully
convolutional recurrent network (MCFCRN) is proposed to exploit the multiple
spatial contexts from the signature feature maps and generate a prediction
sequence while completely avoiding the difficult segmentation problem.
Furthermore, an implicit language model is developed to make predictions based
on semantic context within a predicting feature sequence, providing a new
perspective for incorporating lexicon constraints and prior knowledge about a
certain language in the recognition procedure. Experiments on two standard
benchmarks, Dataset-CASIA and Dataset-ICDAR, yielded outstanding results, with
correct rates of 97.10% and 97.15%, respectively, which are significantly
better than the best result reported thus far in the literature.
| [
{
"version": "v1",
"created": "Sun, 9 Oct 2016 02:39:07 GMT"
},
{
"version": "v2",
"created": "Thu, 25 May 2017 15:33:19 GMT"
}
] | 2017-05-26T00:00:00 | [
[
"Xie",
"Zecheng",
""
],
[
"Sun",
"Zenghui",
""
],
[
"Jin",
"Lianwen",
""
],
[
"Ni",
"Hao",
""
],
[
"Lyons",
"Terry",
""
]
] | TITLE: Learning Spatial-Semantic Context with Fully Convolutional Recurrent
Network for Online Handwritten Chinese Text Recognition
ABSTRACT: Online handwritten Chinese text recognition (OHCTR) is a challenging problem
as it involves a large-scale character set, ambiguous segmentation, and
variable-length input sequences. In this paper, we exploit the outstanding
capability of path signature to translate online pen-tip trajectories into
informative signature feature maps using a sliding window-based method,
successfully capturing the analytic and geometric properties of pen strokes
with strong local invariance and robustness. A multi-spatial-context fully
convolutional recurrent network (MCFCRN) is proposed to exploit the multiple
spatial contexts from the signature feature maps and generate a prediction
sequence while completely avoiding the difficult segmentation problem.
Furthermore, an implicit language model is developed to make predictions based
on semantic context within a predicting feature sequence, providing a new
perspective for incorporating lexicon constraints and prior knowledge about a
certain language in the recognition procedure. Experiments on two standard
benchmarks, Dataset-CASIA and Dataset-ICDAR, yielded outstanding results, with
correct rates of 97.10% and 97.15%, respectively, which are significantly
better than the best result reported thus far in the literature.
| no_new_dataset | 0.950088 |
1611.01086 | Shohreh Shaghaghian Ms | Shohreh Shaghaghian, Mark Coates | Online Bayesian Inference of Diffusion Networks | null | null | null | null | cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the process by which a contagion disseminates throughout a
network is of great importance in many real world applications. The required
sophistication of the inference approach depends on the type of information we
want to extract as well as the number of observations that are available to us.
We analyze scenarios in which not only the underlying network structure
(parental relationships and link strengths) needs to be detected, but also the
infection times must be estimated. We assume that our only observation of the
diffusion process is a set of time series, one for each node of the network,
which exhibit changepoints when an infection occurs. After formulating a model
to describe the contagion, and selecting appropriate prior distributions, we
seek to find the set of model parameters that best explains our observations.
Modeling the problem in a Bayesian framework, we exploit Monte Carlo Markov
Chain,
Sequential Monte Carlo, and time series analysis techniques to develop batch
and online inference algorithms. We evaluate the performance of our proposed
algorithms via numerical simulations of synthetic network contagions and
analysis of real-world datasets.
| [
{
"version": "v1",
"created": "Thu, 3 Nov 2016 16:41:02 GMT"
},
{
"version": "v2",
"created": "Thu, 25 May 2017 01:16:05 GMT"
}
] | 2017-05-26T00:00:00 | [
[
"Shaghaghian",
"Shohreh",
""
],
[
"Coates",
"Mark",
""
]
] | TITLE: Online Bayesian Inference of Diffusion Networks
ABSTRACT: Understanding the process by which a contagion disseminates throughout a
network is of great importance in many real world applications. The required
sophistication of the inference approach depends on the type of information we
want to extract as well as the number of observations that are available to us.
We analyze scenarios in which not only the underlying network structure
(parental relationships and link strengths) needs to be detected, but also the
infection times must be estimated. We assume that our only observation of the
diffusion process is a set of time series, one for each node of the network,
which exhibit changepoints when an infection occurs. After formulating a model
to describe the contagion, and selecting appropriate prior distributions, we
seek to find the set of model parameters that best explains our observations.
Modeling the problem in a Bayesian framework, we exploit Monte Carlo Markov
Chain,
Sequential Monte Carlo, and time series analysis techniques to develop batch
and online inference algorithms. We evaluate the performance of our proposed
algorithms via numerical simulations of synthetic network contagions and
analysis of real-world datasets.
| no_new_dataset | 0.947817 |
1704.01704 | Yi Han | Yi Han, Benjamin I. P. Rubinstein | Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks | 10 pages, 7 figures, 10 tables | null | null | null | cs.CR cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the wide use of machine learning in adversarial settings including
computer security, recent studies have demonstrated vulnerabilities to evasion
attacks---carefully crafted adversarial samples that closely resemble
legitimate instances, but cause misclassification. In this paper, we examine
the adequacy of the leading approach to generating adversarial samples---the
gradient descent approach. In particular (1) we perform extensive experiments
on three datasets, MNIST, USPS and Spambase, in order to analyse the
effectiveness of the gradient-descent method against non-linear support vector
machines, and conclude that carefully reduced kernel smoothness can
significantly increase robustness to the attack; (2) we demonstrate that
separated inter-class support vectors lead to more secure models, and propose a
quantity similar to margin that can efficiently predict potential
susceptibility to gradient-descent attacks, before the attack is launched; and
(3) we design a new adversarial sample construction algorithm based on
optimising the multiplicative ratio of class decision functions.
| [
{
"version": "v1",
"created": "Thu, 6 Apr 2017 04:35:40 GMT"
},
{
"version": "v2",
"created": "Thu, 25 May 2017 04:32:43 GMT"
}
] | 2017-05-26T00:00:00 | [
[
"Han",
"Yi",
""
],
[
"Rubinstein",
"Benjamin I. P.",
""
]
] | TITLE: Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks
ABSTRACT: Despite the wide use of machine learning in adversarial settings including
computer security, recent studies have demonstrated vulnerabilities to evasion
attacks---carefully crafted adversarial samples that closely resemble
legitimate instances, but cause misclassification. In this paper, we examine
the adequacy of the leading approach to generating adversarial samples---the
gradient descent approach. In particular (1) we perform extensive experiments
on three datasets, MNIST, USPS and Spambase, in order to analyse the
effectiveness of the gradient-descent method against non-linear support vector
machines, and conclude that carefully reduced kernel smoothness can
significantly increase robustness to the attack; (2) we demonstrate that
separated inter-class support vectors lead to more secure models, and propose a
quantity similar to margin that can efficiently predict potential
susceptibility to gradient-descent attacks, before the attack is launched; and
(3) we design a new adversarial sample construction algorithm based on
optimising the multiplicative ratio of class decision functions.
| no_new_dataset | 0.947866 |
1704.08772 | Grigorios Chrysos | Grigorios G. Chrysos, Stefanos Zafeiriou | Deep Face Deblurring | null | null | null | null | cs.CV cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Blind deblurring consists a long studied task, however the outcomes of
generic methods are not effective in real world blurred images. Domain-specific
methods for deblurring targeted object categories, e.g. text or faces,
frequently outperform their generic counterparts, hence they are attracting an
increasing amount of attention. In this work, we develop such a domain-specific
method to tackle deblurring of human faces, henceforth referred to as face
deblurring. Studying faces is of tremendous significance in computer vision,
however face deblurring has yet to demonstrate some convincing results. This
can be partly attributed to the combination of i) poor texture and ii) highly
structure shape that yield the contour/gradient priors (that are typically
used) sub-optimal. In our work instead of making assumptions over the prior, we
adopt a learning approach by inserting weak supervision that exploits the
well-documented structure of the face. Namely, we utilise a deep network to
perform the deblurring and employ a face alignment technique to pre-process
each face. We additionally surpass the requirement of the deep network for
thousands training samples, by introducing an efficient framework that allows
the generation of a large dataset. We utilised this framework to create 2MF2, a
dataset of over two million frames. We conducted experiments with real world
blurred facial images and report that our method returns a result close to the
sharp natural latent image.
| [
{
"version": "v1",
"created": "Thu, 27 Apr 2017 23:01:45 GMT"
},
{
"version": "v2",
"created": "Thu, 25 May 2017 07:45:36 GMT"
}
] | 2017-05-26T00:00:00 | [
[
"Chrysos",
"Grigorios G.",
""
],
[
"Zafeiriou",
"Stefanos",
""
]
] | TITLE: Deep Face Deblurring
ABSTRACT: Blind deblurring consists a long studied task, however the outcomes of
generic methods are not effective in real world blurred images. Domain-specific
methods for deblurring targeted object categories, e.g. text or faces,
frequently outperform their generic counterparts, hence they are attracting an
increasing amount of attention. In this work, we develop such a domain-specific
method to tackle deblurring of human faces, henceforth referred to as face
deblurring. Studying faces is of tremendous significance in computer vision,
however face deblurring has yet to demonstrate some convincing results. This
can be partly attributed to the combination of i) poor texture and ii) highly
structure shape that yield the contour/gradient priors (that are typically
used) sub-optimal. In our work instead of making assumptions over the prior, we
adopt a learning approach by inserting weak supervision that exploits the
well-documented structure of the face. Namely, we utilise a deep network to
perform the deblurring and employ a face alignment technique to pre-process
each face. We additionally surpass the requirement of the deep network for
thousands training samples, by introducing an efficient framework that allows
the generation of a large dataset. We utilised this framework to create 2MF2, a
dataset of over two million frames. We conducted experiments with real world
blurred facial images and report that our method returns a result close to the
sharp natural latent image.
| new_dataset | 0.892234 |
1705.08923 | Tao Zhou | Tao Zhou, Muhao Chen, Jie Yu, Demetri Terzopoulos | Attention-based Natural Language Person Retrieval | CVPR 2017 Workshop (vision meets cognition) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Following the recent progress in image classification and captioning using
deep learning, we develop a novel natural language person retrieval system
based on an attention mechanism. More specifically, given the description of a
person, the goal is to localize the person in an image. To this end, we first
construct a benchmark dataset for natural language person retrieval. To do so,
we generate bounding boxes for persons in a public image dataset from the
segmentation masks, which are then annotated with descriptions and attributes
using the Amazon Mechanical Turk. We then adopt a region proposal network in
Faster R-CNN as a candidate region generator. The cropped images based on the
region proposals as well as the whole images with attention weights are fed
into Convolutional Neural Networks for visual feature extraction, while the
natural language expression and attributes are input to Bidirectional Long
Short- Term Memory (BLSTM) models for text feature extraction. The visual and
text features are integrated to score region proposals, and the one with the
highest score is retrieved as the output of our system. The experimental
results show significant improvement over the state-of-the-art method for
generic object retrieval and this line of research promises to benefit search
in surveillance video footage.
| [
{
"version": "v1",
"created": "Wed, 24 May 2017 18:36:58 GMT"
}
] | 2017-05-26T00:00:00 | [
[
"Zhou",
"Tao",
""
],
[
"Chen",
"Muhao",
""
],
[
"Yu",
"Jie",
""
],
[
"Terzopoulos",
"Demetri",
""
]
] | TITLE: Attention-based Natural Language Person Retrieval
ABSTRACT: Following the recent progress in image classification and captioning using
deep learning, we develop a novel natural language person retrieval system
based on an attention mechanism. More specifically, given the description of a
person, the goal is to localize the person in an image. To this end, we first
construct a benchmark dataset for natural language person retrieval. To do so,
we generate bounding boxes for persons in a public image dataset from the
segmentation masks, which are then annotated with descriptions and attributes
using the Amazon Mechanical Turk. We then adopt a region proposal network in
Faster R-CNN as a candidate region generator. The cropped images based on the
region proposals as well as the whole images with attention weights are fed
into Convolutional Neural Networks for visual feature extraction, while the
natural language expression and attributes are input to Bidirectional Long
Short- Term Memory (BLSTM) models for text feature extraction. The visual and
text features are integrated to score region proposals, and the one with the
highest score is retrieved as the output of our system. The experimental
results show significant improvement over the state-of-the-art method for
generic object retrieval and this line of research promises to benefit search
in surveillance video footage.
| new_dataset | 0.941708 |
1705.08982 | Shuai Xiao | Shuai Xiao, Junchi Yan, Stephen M. Chu, Xiaokang Yang, Hongyuan Zha | Modeling The Intensity Function Of Point Process Via Recurrent Neural
Networks | Accepted at Thirty-First AAAI Conference on Artificial Intelligence
(AAAI17) | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Event sequence, asynchronously generated with random timestamp, is ubiquitous
among applications. The precise and arbitrary timestamp can carry important
clues about the underlying dynamics, and has lent the event data fundamentally
different from the time-series whereby series is indexed with fixed and equal
time interval. One expressive mathematical tool for modeling event is point
process. The intensity functions of many point processes involve two
components: the background and the effect by the history. Due to its inherent
spontaneousness, the background can be treated as a time series while the other
need to handle the history events. In this paper, we model the background by a
Recurrent Neural Network (RNN) with its units aligned with time series indexes
while the history effect is modeled by another RNN whose units are aligned with
asynchronous events to capture the long-range dynamics. The whole model with
event type and timestamp prediction output layers can be trained end-to-end.
Our approach takes an RNN perspective to point process, and models its
background and history effect. For utility, our method allows a black-box
treatment for modeling the intensity which is often a pre-defined parametric
form in point processes. Meanwhile end-to-end training opens the venue for
reusing existing rich techniques in deep network for point process modeling. We
apply our model to the predictive maintenance problem using a log dataset by
more than 1000 ATMs from a global bank headquartered in North America.
| [
{
"version": "v1",
"created": "Wed, 24 May 2017 22:23:14 GMT"
}
] | 2017-05-26T00:00:00 | [
[
"Xiao",
"Shuai",
""
],
[
"Yan",
"Junchi",
""
],
[
"Chu",
"Stephen M.",
""
],
[
"Yang",
"Xiaokang",
""
],
[
"Zha",
"Hongyuan",
""
]
] | TITLE: Modeling The Intensity Function Of Point Process Via Recurrent Neural
Networks
ABSTRACT: Event sequence, asynchronously generated with random timestamp, is ubiquitous
among applications. The precise and arbitrary timestamp can carry important
clues about the underlying dynamics, and has lent the event data fundamentally
different from the time-series whereby series is indexed with fixed and equal
time interval. One expressive mathematical tool for modeling event is point
process. The intensity functions of many point processes involve two
components: the background and the effect by the history. Due to its inherent
spontaneousness, the background can be treated as a time series while the other
need to handle the history events. In this paper, we model the background by a
Recurrent Neural Network (RNN) with its units aligned with time series indexes
while the history effect is modeled by another RNN whose units are aligned with
asynchronous events to capture the long-range dynamics. The whole model with
event type and timestamp prediction output layers can be trained end-to-end.
Our approach takes an RNN perspective to point process, and models its
background and history effect. For utility, our method allows a black-box
treatment for modeling the intensity which is often a pre-defined parametric
form in point processes. Meanwhile end-to-end training opens the venue for
reusing existing rich techniques in deep network for point process modeling. We
apply our model to the predictive maintenance problem using a log dataset by
more than 1000 ATMs from a global bank headquartered in North America.
| no_new_dataset | 0.952838 |
1705.08994 | Hassan Jameel Asghar | Hassan Jameel Asghar, Paul Tyler, Mohamed Ali Kaafar | On the Privacy of the Opal Data Release: A Response | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This document is a response to a report from the University of Melbourne on
the privacy of the Opal dataset release. The Opal dataset was released by
Data61 (CSIRO) in conjunction with the Transport for New South Wales (TfNSW).
The data consists of two separate weeks of "tap-on/tap-off" data of individuals
who used any of the four different modes of public transport from TfNSW: buses,
light rail, train and ferries. These taps are recorded through the smart
ticketing system, known as Opal, available in the state of New South Wales,
Australia.
| [
{
"version": "v1",
"created": "Wed, 24 May 2017 23:11:13 GMT"
}
] | 2017-05-26T00:00:00 | [
[
"Asghar",
"Hassan Jameel",
""
],
[
"Tyler",
"Paul",
""
],
[
"Kaafar",
"Mohamed Ali",
""
]
] | TITLE: On the Privacy of the Opal Data Release: A Response
ABSTRACT: This document is a response to a report from the University of Melbourne on
the privacy of the Opal dataset release. The Opal dataset was released by
Data61 (CSIRO) in conjunction with the Transport for New South Wales (TfNSW).
The data consists of two separate weeks of "tap-on/tap-off" data of individuals
who used any of the four different modes of public transport from TfNSW: buses,
light rail, train and ferries. These taps are recorded through the smart
ticketing system, known as Opal, available in the state of New South Wales,
Australia.
| no_new_dataset | 0.88136 |
1705.09054 | Zhipeng Xie | Zhipeng Xie and Junfeng Hu | Max-Cosine Matching Based Neural Models for Recognizing Textual
Entailment | null | DASFAA (1) 2017: 295-308 | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recognizing textual entailment is a fundamental task in a variety of text
mining or natural language processing applications. This paper proposes a
simple neural model for RTE problem. It first matches each word in the
hypothesis with its most-similar word in the premise, producing an augmented
representation of the hypothesis conditioned on the premise as a sequence of
word pairs. The LSTM model is then used to model this augmented sequence, and
the final output from the LSTM is fed into a softmax layer to make the
prediction. Besides the base model, in order to enhance its performance, we
also proposed three techniques: the integration of multiple word-embedding
library, bi-way integration, and ensemble based on model averaging.
Experimental results on the SNLI dataset have shown that the three techniques
are effective in boosting the predicative accuracy and that our method
outperforms several state-of-the-state ones.
| [
{
"version": "v1",
"created": "Thu, 25 May 2017 05:45:42 GMT"
}
] | 2017-05-26T00:00:00 | [
[
"Xie",
"Zhipeng",
""
],
[
"Hu",
"Junfeng",
""
]
] | TITLE: Max-Cosine Matching Based Neural Models for Recognizing Textual
Entailment
ABSTRACT: Recognizing textual entailment is a fundamental task in a variety of text
mining or natural language processing applications. This paper proposes a
simple neural model for RTE problem. It first matches each word in the
hypothesis with its most-similar word in the premise, producing an augmented
representation of the hypothesis conditioned on the premise as a sequence of
word pairs. The LSTM model is then used to model this augmented sequence, and
the final output from the LSTM is fed into a softmax layer to make the
prediction. Besides the base model, in order to enhance its performance, we
also proposed three techniques: the integration of multiple word-embedding
library, bi-way integration, and ensemble based on model averaging.
Experimental results on the SNLI dataset have shown that the three techniques
are effective in boosting the predicative accuracy and that our method
outperforms several state-of-the-state ones.
| no_new_dataset | 0.948155 |
1705.09058 | Yihui He | Yihui He, Ming Xiang | An Empirical Analysis of Approximation Algorithms for the Euclidean
Traveling Salesman Problem | 4 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With applications to many disciplines, the traveling salesman problem (TSP)
is a classical computer science optimization problem with applications to
industrial engineering, theoretical computer science, bioinformatics, and
several other disciplines. In recent years, there have been a plethora of novel
approaches for approximate solutions ranging from simplistic greedy to
cooperative distributed algorithms derived from artificial intelligence. In
this paper, we perform an evaluation and analysis of cornerstone algorithms for
the Euclidean TSP. We evaluate greedy, 2-opt, and genetic algorithms. We use
several datasets as input for the algorithms including a small dataset, a
mediumsized dataset representing cities in the United States, and a synthetic
dataset consisting of 200 cities to test algorithm scalability. We discover
that the greedy and 2-opt algorithms efficiently calculate solutions for
smaller datasets. Genetic algorithm has the best performance for optimality for
medium to large datasets, but generally have longer runtime. Our
implementations is public available.
| [
{
"version": "v1",
"created": "Thu, 25 May 2017 06:21:39 GMT"
}
] | 2017-05-26T00:00:00 | [
[
"He",
"Yihui",
""
],
[
"Xiang",
"Ming",
""
]
] | TITLE: An Empirical Analysis of Approximation Algorithms for the Euclidean
Traveling Salesman Problem
ABSTRACT: With applications to many disciplines, the traveling salesman problem (TSP)
is a classical computer science optimization problem with applications to
industrial engineering, theoretical computer science, bioinformatics, and
several other disciplines. In recent years, there have been a plethora of novel
approaches for approximate solutions ranging from simplistic greedy to
cooperative distributed algorithms derived from artificial intelligence. In
this paper, we perform an evaluation and analysis of cornerstone algorithms for
the Euclidean TSP. We evaluate greedy, 2-opt, and genetic algorithms. We use
several datasets as input for the algorithms including a small dataset, a
mediumsized dataset representing cities in the United States, and a synthetic
dataset consisting of 200 cities to test algorithm scalability. We discover
that the greedy and 2-opt algorithms efficiently calculate solutions for
smaller datasets. Genetic algorithm has the best performance for optimality for
medium to large datasets, but generally have longer runtime. Our
implementations is public available.
| new_dataset | 0.826432 |
1705.09142 | Konda Reddy Mopuri | Konda Reddy Mopuri, Vishal B. Athreya and R. Venkatesh Babu | Deep image representations using caption generators | ICME 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep learning exploits large volumes of labeled data to learn powerful
models. When the target dataset is small, it is a common practice to perform
transfer learning using pre-trained models to learn new task specific
representations. However, pre-trained CNNs for image recognition are provided
with limited information about the image during training, which is label alone.
Tasks such as scene retrieval suffer from features learned from this weak
supervision and require stronger supervision to better understand the contents
of the image. In this paper, we exploit the features learned from caption
generating models to learn novel task specific image representations. In
particular, we consider the state-of-the art captioning system Show and
Tell~\cite{SnT-pami-2016} and the dense region description model
DenseCap~\cite{densecap-cvpr-2016}. We demonstrate that, owing to richer
supervision provided during the process of training, the features learned by
the captioning system perform better than those of CNNs. Further, we train a
siamese network with a modified pair-wise loss to fuse the features learned
by~\cite{SnT-pami-2016} and~\cite{densecap-cvpr-2016} and learn image
representations suitable for retrieval. Experiments show that the proposed
fusion exploits the complementary nature of the individual features and yields
state-of-the art retrieval results on benchmark datasets.
| [
{
"version": "v1",
"created": "Thu, 25 May 2017 12:13:27 GMT"
}
] | 2017-05-26T00:00:00 | [
[
"Mopuri",
"Konda Reddy",
""
],
[
"Athreya",
"Vishal B.",
""
],
[
"Babu",
"R. Venkatesh",
""
]
] | TITLE: Deep image representations using caption generators
ABSTRACT: Deep learning exploits large volumes of labeled data to learn powerful
models. When the target dataset is small, it is a common practice to perform
transfer learning using pre-trained models to learn new task specific
representations. However, pre-trained CNNs for image recognition are provided
with limited information about the image during training, which is label alone.
Tasks such as scene retrieval suffer from features learned from this weak
supervision and require stronger supervision to better understand the contents
of the image. In this paper, we exploit the features learned from caption
generating models to learn novel task specific image representations. In
particular, we consider the state-of-the art captioning system Show and
Tell~\cite{SnT-pami-2016} and the dense region description model
DenseCap~\cite{densecap-cvpr-2016}. We demonstrate that, owing to richer
supervision provided during the process of training, the features learned by
the captioning system perform better than those of CNNs. Further, we train a
siamese network with a modified pair-wise loss to fuse the features learned
by~\cite{SnT-pami-2016} and~\cite{densecap-cvpr-2016} and learn image
representations suitable for retrieval. Experiments show that the proposed
fusion exploits the complementary nature of the individual features and yields
state-of-the art retrieval results on benchmark datasets.
| no_new_dataset | 0.948775 |
1705.09269 | Joseph Anderson | Joseph Anderson | Geometric Methods for Robust Data Analysis in High Dimension | 180 Pages, 7 Figures, PhD thesis, Ohio State (2017) | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine learning and data analysis now finds both scientific and industrial
application in biology, chemistry, geology, medicine, and physics. These
applications rely on large quantities of data gathered from automated sensors
and user input. Furthermore, the dimensionality of many datasets is extreme:
more details are being gathered about single user interactions or sensor
readings. All of these applications encounter problems with a common theme: use
observed data to make inferences about the world. Our work obtains the first
provably efficient algorithms for Independent Component Analysis (ICA) in the
presence of heavy-tailed data. The main tool in this result is the centroid
body (a well-known topic in convex geometry), along with optimization and
random walks for sampling from a convex body. This is the first algorithmic use
of the centroid body and it is of independent theoretical interest, since it
effectively replaces the estimation of covariance from samples, and is more
generally accessible.
This reduction relies on a non-linear transformation of samples from such an
intersection of halfspaces (i.e. a simplex) to samples which are approximately
from a linearly transformed product distribution. Through this transformation
of samples, which can be done efficiently, one can then use an ICA algorithm to
recover the vertices of the intersection of halfspaces.
Finally, we again use ICA as an algorithmic primitive to construct an
efficient solution to the widely-studied problem of learning the parameters of
a Gaussian mixture model. Our algorithm again transforms samples from a
Gaussian mixture model into samples which fit into the ICA model and, when
processed by an ICA algorithm, result in recovery of the mixture parameters.
Our algorithm is effective even when the number of Gaussians in the mixture
grows polynomially with the ambient dimension
| [
{
"version": "v1",
"created": "Thu, 25 May 2017 17:25:04 GMT"
}
] | 2017-05-26T00:00:00 | [
[
"Anderson",
"Joseph",
""
]
] | TITLE: Geometric Methods for Robust Data Analysis in High Dimension
ABSTRACT: Machine learning and data analysis now finds both scientific and industrial
application in biology, chemistry, geology, medicine, and physics. These
applications rely on large quantities of data gathered from automated sensors
and user input. Furthermore, the dimensionality of many datasets is extreme:
more details are being gathered about single user interactions or sensor
readings. All of these applications encounter problems with a common theme: use
observed data to make inferences about the world. Our work obtains the first
provably efficient algorithms for Independent Component Analysis (ICA) in the
presence of heavy-tailed data. The main tool in this result is the centroid
body (a well-known topic in convex geometry), along with optimization and
random walks for sampling from a convex body. This is the first algorithmic use
of the centroid body and it is of independent theoretical interest, since it
effectively replaces the estimation of covariance from samples, and is more
generally accessible.
This reduction relies on a non-linear transformation of samples from such an
intersection of halfspaces (i.e. a simplex) to samples which are approximately
from a linearly transformed product distribution. Through this transformation
of samples, which can be done efficiently, one can then use an ICA algorithm to
recover the vertices of the intersection of halfspaces.
Finally, we again use ICA as an algorithmic primitive to construct an
efficient solution to the widely-studied problem of learning the parameters of
a Gaussian mixture model. Our algorithm again transforms samples from a
Gaussian mixture model into samples which fit into the ICA model and, when
processed by an ICA algorithm, result in recovery of the mixture parameters.
Our algorithm is effective even when the number of Gaussians in the mixture
grows polynomially with the ambient dimension
| no_new_dataset | 0.939858 |
1511.05286 | Oren Kraus | Oren Z. Kraus, Lei Jimmy Ba, Brendan Frey | Classifying and Segmenting Microscopy Images Using Convolutional
Multiple Instance Learning | null | Bioinformatics (2016) 32 (12): i52-i59 | 10.1093/bioinformatics/btw252 | null | cs.CV q-bio.SC stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Convolutional neural networks (CNN) have achieved state of the art
performance on both classification and segmentation tasks. Applying CNNs to
microscopy images is challenging due to the lack of datasets labeled at the
single cell level. We extend the application of CNNs to microscopy image
classification and segmentation using multiple instance learning (MIL). We
present the adaptive Noisy-AND MIL pooling function, a new MIL operator that is
robust to outliers. Combining CNNs with MIL enables training CNNs using full
resolution microscopy images with global labels. We base our approach on the
similarity between the aggregation function used in MIL and pooling layers used
in CNNs. We show that training MIL CNNs end-to-end outperforms several previous
methods on both mammalian and yeast microscopy images without requiring any
segmentation steps.
| [
{
"version": "v1",
"created": "Tue, 17 Nov 2015 06:55:58 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Kraus",
"Oren Z.",
""
],
[
"Ba",
"Lei Jimmy",
""
],
[
"Frey",
"Brendan",
""
]
] | TITLE: Classifying and Segmenting Microscopy Images Using Convolutional
Multiple Instance Learning
ABSTRACT: Convolutional neural networks (CNN) have achieved state of the art
performance on both classification and segmentation tasks. Applying CNNs to
microscopy images is challenging due to the lack of datasets labeled at the
single cell level. We extend the application of CNNs to microscopy image
classification and segmentation using multiple instance learning (MIL). We
present the adaptive Noisy-AND MIL pooling function, a new MIL operator that is
robust to outliers. Combining CNNs with MIL enables training CNNs using full
resolution microscopy images with global labels. We base our approach on the
similarity between the aggregation function used in MIL and pooling layers used
in CNNs. We show that training MIL CNNs end-to-end outperforms several previous
methods on both mammalian and yeast microscopy images without requiring any
segmentation steps.
| no_new_dataset | 0.956227 |
1603.01508 | Robert Kleinberg | Arpita Ghosh and Robert Kleinberg | Inferential Privacy Guarantees for Differentially Private Mechanisms | null | null | null | null | cs.DS cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The correlations and network structure amongst individuals in datasets
today---whether explicitly articulated, or deduced from biological or
behavioral connections---pose new issues around privacy guarantees, because of
inferences that can be made about one individual from another's data. This
motivates quantifying privacy in networked contexts in terms of "inferential
privacy"---which measures the change in beliefs about an individual's data from
the result of a computation---as originally proposed by Dalenius in the 1970's.
Inferential privacy is implied by differential privacy when data are
independent, but can be much worse when data are correlated; indeed, simple
examples, as well as a general impossibility theorem of Dwork and Naor,
preclude the possibility of achieving non-trivial inferential privacy when the
adversary can have arbitrary auxiliary information. In this paper, we ask how
differential privacy guarantees translate to guarantees on inferential privacy
in networked contexts: specifically, under what limitations on the adversary's
information about correlations, modeled as a prior distribution over datasets,
can we deduce an inferential guarantee from a differential one?
We prove two main results. The first result pertains to distributions that
satisfy a natural positive-affiliation condition, and gives an upper bound on
the inferential privacy guarantee for any differentially private mechanism.
This upper bound is matched by a simple mechanism that adds Laplace noise to
the sum of the data. The second result pertains to distributions that have weak
correlations, defined in terms of a suitable "influence matrix". The result
provides an upper bound for inferential privacy in terms of the differential
privacy parameter and the spectral norm of this matrix.
| [
{
"version": "v1",
"created": "Fri, 4 Mar 2016 15:50:24 GMT"
},
{
"version": "v2",
"created": "Tue, 23 May 2017 18:52:06 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Ghosh",
"Arpita",
""
],
[
"Kleinberg",
"Robert",
""
]
] | TITLE: Inferential Privacy Guarantees for Differentially Private Mechanisms
ABSTRACT: The correlations and network structure amongst individuals in datasets
today---whether explicitly articulated, or deduced from biological or
behavioral connections---pose new issues around privacy guarantees, because of
inferences that can be made about one individual from another's data. This
motivates quantifying privacy in networked contexts in terms of "inferential
privacy"---which measures the change in beliefs about an individual's data from
the result of a computation---as originally proposed by Dalenius in the 1970's.
Inferential privacy is implied by differential privacy when data are
independent, but can be much worse when data are correlated; indeed, simple
examples, as well as a general impossibility theorem of Dwork and Naor,
preclude the possibility of achieving non-trivial inferential privacy when the
adversary can have arbitrary auxiliary information. In this paper, we ask how
differential privacy guarantees translate to guarantees on inferential privacy
in networked contexts: specifically, under what limitations on the adversary's
information about correlations, modeled as a prior distribution over datasets,
can we deduce an inferential guarantee from a differential one?
We prove two main results. The first result pertains to distributions that
satisfy a natural positive-affiliation condition, and gives an upper bound on
the inferential privacy guarantee for any differentially private mechanism.
This upper bound is matched by a simple mechanism that adds Laplace noise to
the sum of the data. The second result pertains to distributions that have weak
correlations, defined in terms of a suitable "influence matrix". The result
provides an upper bound for inferential privacy in terms of the differential
privacy parameter and the spectral norm of this matrix.
| no_new_dataset | 0.9455 |
1605.05197 | Philippe Weinzaepfel | Philippe Weinzaepfel, Xavier Martin, Cordelia Schmid | Human Action Localization with Sparse Spatial Supervision | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce an approach for spatio-temporal human action localization using
sparse spatial supervision. Our method leverages the large amount of annotated
humans available today and extracts human tubes by combining a state-of-the-art
human detector with a tracking-by-detection approach. Given these high-quality
human tubes and temporal supervision, we select positive and negative tubes
with very sparse spatial supervision, i.e., only one spatially annotated frame
per instance. The selected tubes allow us to effectively learn a
spatio-temporal action detector based on dense trajectories or CNNs. We conduct
experiments on existing action localization benchmarks: UCF-Sports, J-HMDB and
UCF-101. Our results show that our approach, despite using sparse spatial
supervision, performs on par with methods using full supervision, i.e., one
bounding box annotation per frame. To further validate our method, we introduce
DALY (Daily Action Localization in YouTube), a dataset for realistic action
localization in space and time. It contains high quality temporal and spatial
annotations for 3.6k instances of 10 actions in 31 hours of videos (3.3M
frames). It is an order of magnitude larger than existing datasets, with more
diversity in appearance and long untrimmed videos.
| [
{
"version": "v1",
"created": "Tue, 17 May 2016 14:55:03 GMT"
},
{
"version": "v2",
"created": "Tue, 23 May 2017 19:19:23 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Weinzaepfel",
"Philippe",
""
],
[
"Martin",
"Xavier",
""
],
[
"Schmid",
"Cordelia",
""
]
] | TITLE: Human Action Localization with Sparse Spatial Supervision
ABSTRACT: We introduce an approach for spatio-temporal human action localization using
sparse spatial supervision. Our method leverages the large amount of annotated
humans available today and extracts human tubes by combining a state-of-the-art
human detector with a tracking-by-detection approach. Given these high-quality
human tubes and temporal supervision, we select positive and negative tubes
with very sparse spatial supervision, i.e., only one spatially annotated frame
per instance. The selected tubes allow us to effectively learn a
spatio-temporal action detector based on dense trajectories or CNNs. We conduct
experiments on existing action localization benchmarks: UCF-Sports, J-HMDB and
UCF-101. Our results show that our approach, despite using sparse spatial
supervision, performs on par with methods using full supervision, i.e., one
bounding box annotation per frame. To further validate our method, we introduce
DALY (Daily Action Localization in YouTube), a dataset for realistic action
localization in space and time. It contains high quality temporal and spatial
annotations for 3.6k instances of 10 actions in 31 hours of videos (3.3M
frames). It is an order of magnitude larger than existing datasets, with more
diversity in appearance and long untrimmed videos.
| new_dataset | 0.956796 |
1609.09560 | Michele Nogueira | Michele Nogueira and Augusto Almeida Santos and Jos\'e M. F. Moura | Early Signals from Volumetric DDoS Attacks: An Empirical Study | null | null | null | null | cs.NI cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Distributed Denial of Service (DDoS) is a common type of Cybercrime. It can
strongly damage a company reputation and increase its costs. Attackers improve
continuously their strategies. They doubled the amount of unleashed
communication requests in volume, size, and frequency in the last few years.
This occurs against different hosts, causing resource exhaustion. Previous
studies focused on detecting or mitigating ongoing DDoS attacks. Yet,
addressing DDoS attacks when they are already in place may be too late. In this
article, we consider network resilience by early prediction of attack trends.
We show empirically the advantage of using non-parametric leading indicators
for early prediction of volumetric DDoS attacks. We report promising results
over a real dataset from CAIDA. Our results raise new questions and
opportunities for further research in early predicting trends of DDoS attacks.
| [
{
"version": "v1",
"created": "Fri, 30 Sep 2016 01:17:07 GMT"
},
{
"version": "v2",
"created": "Tue, 23 May 2017 22:48:04 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Nogueira",
"Michele",
""
],
[
"Santos",
"Augusto Almeida",
""
],
[
"Moura",
"José M. F.",
""
]
] | TITLE: Early Signals from Volumetric DDoS Attacks: An Empirical Study
ABSTRACT: Distributed Denial of Service (DDoS) is a common type of Cybercrime. It can
strongly damage a company reputation and increase its costs. Attackers improve
continuously their strategies. They doubled the amount of unleashed
communication requests in volume, size, and frequency in the last few years.
This occurs against different hosts, causing resource exhaustion. Previous
studies focused on detecting or mitigating ongoing DDoS attacks. Yet,
addressing DDoS attacks when they are already in place may be too late. In this
article, we consider network resilience by early prediction of attack trends.
We show empirically the advantage of using non-parametric leading indicators
for early prediction of volumetric DDoS attacks. We report promising results
over a real dataset from CAIDA. Our results raise new questions and
opportunities for further research in early predicting trends of DDoS attacks.
| no_new_dataset | 0.948585 |
1611.02737 | Jaroslaw Szlichta | Sridevi Baskaran, Alexander Keller, Fei Chiang, Golab Lukasz, Jaroslaw
Szlichta | Efficient Discovery of Ontology Functional Dependencies | 12 pages | null | null | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Poor data quality has become a pervasive issue due to the increasing
complexity and size of modern datasets. Constraint based data cleaning
techniques rely on integrity constraints as a benchmark to identify and correct
errors. Data values that do not satisfy the given set of constraints are
flagged as dirty, and data updates are made to re-align the data and the
constraints. However, many errors often require user input to resolve due to
domain expertise defining specific terminology and relationships. For example,
in pharmaceuticals, 'Advil' \emph{is-a} brand name for 'ibuprofen' that can be
captured in a pharmaceutical ontology. While functional dependencies (FDs) have
traditionally been used in existing data cleaning solutions to model syntactic
equivalence, they are not able to model broader relationships (e.g., is-a)
defined by an ontology. In this paper, we take a first step towards extending
the set of data quality constraints used in data cleaning by defining and
discovering \emph{Ontology Functional Dependencies} (OFDs). We lay out
theoretical and practical foundations for OFDs, including a set of sound and
complete axioms, and a linear inference procedure. We then develop effective
algorithms for discovering OFDs, and a set of optimizations that efficiently
prune the search space. Our experimental evaluation using real data show the
scalability and accuracy of our algorithms.
| [
{
"version": "v1",
"created": "Tue, 8 Nov 2016 22:03:35 GMT"
},
{
"version": "v2",
"created": "Wed, 16 Nov 2016 05:13:36 GMT"
},
{
"version": "v3",
"created": "Wed, 24 May 2017 01:44:45 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Baskaran",
"Sridevi",
""
],
[
"Keller",
"Alexander",
""
],
[
"Chiang",
"Fei",
""
],
[
"Lukasz",
"Golab",
""
],
[
"Szlichta",
"Jaroslaw",
""
]
] | TITLE: Efficient Discovery of Ontology Functional Dependencies
ABSTRACT: Poor data quality has become a pervasive issue due to the increasing
complexity and size of modern datasets. Constraint based data cleaning
techniques rely on integrity constraints as a benchmark to identify and correct
errors. Data values that do not satisfy the given set of constraints are
flagged as dirty, and data updates are made to re-align the data and the
constraints. However, many errors often require user input to resolve due to
domain expertise defining specific terminology and relationships. For example,
in pharmaceuticals, 'Advil' \emph{is-a} brand name for 'ibuprofen' that can be
captured in a pharmaceutical ontology. While functional dependencies (FDs) have
traditionally been used in existing data cleaning solutions to model syntactic
equivalence, they are not able to model broader relationships (e.g., is-a)
defined by an ontology. In this paper, we take a first step towards extending
the set of data quality constraints used in data cleaning by defining and
discovering \emph{Ontology Functional Dependencies} (OFDs). We lay out
theoretical and practical foundations for OFDs, including a set of sound and
complete axioms, and a linear inference procedure. We then develop effective
algorithms for discovering OFDs, and a set of optimizations that efficiently
prune the search space. Our experimental evaluation using real data show the
scalability and accuracy of our algorithms.
| no_new_dataset | 0.949669 |
1611.04308 | Panos Parchas Mr | Panos Parchas, Nikolaos Papailiou, Dimitris Papadias, Francesco Bonchi | Uncertain Graph Sparsification | null | null | null | null | cs.DS cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Uncertain graphs are prevalent in several applications including
communications systems, biological databases and social networks. The ever
increasing size of the underlying data renders both graph storage and query
processing extremely expensive. Sparsification has often been used to reduce
the size of deterministic graphs by maintaining only the important edges.
However, adaptation of deterministic sparsification methods fails in the
uncertain setting. To overcome this problem, we introduce the first
sparsification techniques aimed explicitly at uncertain graphs. The proposed
methods reduce the number of edges and redistribute their probabilities in
order to decrease the graph size, while preserving its underlying structure.
The resulting graph can be used to efficiently and accurately approximate any
query and mining tasks on the original graph. An extensive experimental
evaluation with real and synthetic datasets illustrates the effectiveness of
our techniques on several common graph tasks, including clustering coefficient,
page rank, reliability and shortest path distance.
| [
{
"version": "v1",
"created": "Mon, 14 Nov 2016 09:58:11 GMT"
},
{
"version": "v2",
"created": "Sun, 29 Jan 2017 11:39:21 GMT"
},
{
"version": "v3",
"created": "Tue, 9 May 2017 11:29:06 GMT"
},
{
"version": "v4",
"created": "Wed, 24 May 2017 05:50:38 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Parchas",
"Panos",
""
],
[
"Papailiou",
"Nikolaos",
""
],
[
"Papadias",
"Dimitris",
""
],
[
"Bonchi",
"Francesco",
""
]
] | TITLE: Uncertain Graph Sparsification
ABSTRACT: Uncertain graphs are prevalent in several applications including
communications systems, biological databases and social networks. The ever
increasing size of the underlying data renders both graph storage and query
processing extremely expensive. Sparsification has often been used to reduce
the size of deterministic graphs by maintaining only the important edges.
However, adaptation of deterministic sparsification methods fails in the
uncertain setting. To overcome this problem, we introduce the first
sparsification techniques aimed explicitly at uncertain graphs. The proposed
methods reduce the number of edges and redistribute their probabilities in
order to decrease the graph size, while preserving its underlying structure.
The resulting graph can be used to efficiently and accurately approximate any
query and mining tasks on the original graph. An extensive experimental
evaluation with real and synthetic datasets illustrates the effectiveness of
our techniques on several common graph tasks, including clustering coefficient,
page rank, reliability and shortest path distance.
| no_new_dataset | 0.947478 |
1612.01988 | Abhishek Kumar | Anant Raj, Abhishek Kumar, Youssef Mroueh, P. Thomas Fletcher,
Bernhard Sch\"olkopf | Local Group Invariant Representations via Orbit Embeddings | AISTATS 2017 accepted version including appendix, 18 pages, 1 figure | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Invariance to nuisance transformations is one of the desirable properties of
effective representations. We consider transformations that form a \emph{group}
and propose an approach based on kernel methods to derive local group invariant
representations. Locality is achieved by defining a suitable probability
distribution over the group which in turn induces distributions in the input
feature space. We learn a decision function over these distributions by
appealing to the powerful framework of kernel methods and generate local
invariant random feature maps via kernel approximations. We show uniform
convergence bounds for kernel approximation and provide excess risk bounds for
learning with these features. We evaluate our method on three real datasets,
including Rotated MNIST and CIFAR-10, and observe that it outperforms competing
kernel based approaches. The proposed method also outperforms deep CNN on
Rotated-MNIST and performs comparably to the recently proposed
group-equivariant CNN.
| [
{
"version": "v1",
"created": "Tue, 6 Dec 2016 20:46:39 GMT"
},
{
"version": "v2",
"created": "Wed, 24 May 2017 16:50:08 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Raj",
"Anant",
""
],
[
"Kumar",
"Abhishek",
""
],
[
"Mroueh",
"Youssef",
""
],
[
"Fletcher",
"P. Thomas",
""
],
[
"Schölkopf",
"Bernhard",
""
]
] | TITLE: Local Group Invariant Representations via Orbit Embeddings
ABSTRACT: Invariance to nuisance transformations is one of the desirable properties of
effective representations. We consider transformations that form a \emph{group}
and propose an approach based on kernel methods to derive local group invariant
representations. Locality is achieved by defining a suitable probability
distribution over the group which in turn induces distributions in the input
feature space. We learn a decision function over these distributions by
appealing to the powerful framework of kernel methods and generate local
invariant random feature maps via kernel approximations. We show uniform
convergence bounds for kernel approximation and provide excess risk bounds for
learning with these features. We evaluate our method on three real datasets,
including Rotated MNIST and CIFAR-10, and observe that it outperforms competing
kernel based approaches. The proposed method also outperforms deep CNN on
Rotated-MNIST and performs comparably to the recently proposed
group-equivariant CNN.
| no_new_dataset | 0.941761 |
1703.03386 | Cristian Danescu-Niculescu-Mizil | William L. Hamilton, Justine Zhang, Cristian Danescu-Niculescu-Mizil,
Dan Jurafsky, Jure Leskovec | Loyalty in Online Communities | Extended version of a paper appearing in the Proceedings of ICWSM
2017 (with the same title); please cite the official ICWSM version | null | null | null | cs.SI cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Loyalty is an essential component of multi-community engagement. When users
have the choice to engage with a variety of different communities, they often
become loyal to just one, focusing on that community at the expense of others.
However, it is unclear how loyalty is manifested in user behavior, or whether
loyalty is encouraged by certain community characteristics.
In this paper we operationalize loyalty as a user-community relation: users
loyal to a community consistently prefer it over all others; loyal communities
retain their loyal users over time. By exploring this relation using a large
dataset of discussion communities from Reddit, we reveal that loyalty is
manifested in remarkably consistent behaviors across a wide spectrum of
communities. Loyal users employ language that signals collective identity and
engage with more esoteric, less popular content, indicating they may play a
curational role in surfacing new material. Loyal communities have denser
user-user interaction networks and lower rates of triadic closure, suggesting
that community-level loyalty is associated with more cohesive interactions and
less fragmentation into subgroups. We exploit these general patterns to predict
future rates of loyalty. Our results show that a user's propensity to become
loyal is apparent from their first interactions with a community, suggesting
that some users are intrinsically loyal from the very beginning.
| [
{
"version": "v1",
"created": "Thu, 9 Mar 2017 18:37:50 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Apr 2017 01:09:26 GMT"
},
{
"version": "v3",
"created": "Wed, 24 May 2017 14:45:13 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Hamilton",
"William L.",
""
],
[
"Zhang",
"Justine",
""
],
[
"Danescu-Niculescu-Mizil",
"Cristian",
""
],
[
"Jurafsky",
"Dan",
""
],
[
"Leskovec",
"Jure",
""
]
] | TITLE: Loyalty in Online Communities
ABSTRACT: Loyalty is an essential component of multi-community engagement. When users
have the choice to engage with a variety of different communities, they often
become loyal to just one, focusing on that community at the expense of others.
However, it is unclear how loyalty is manifested in user behavior, or whether
loyalty is encouraged by certain community characteristics.
In this paper we operationalize loyalty as a user-community relation: users
loyal to a community consistently prefer it over all others; loyal communities
retain their loyal users over time. By exploring this relation using a large
dataset of discussion communities from Reddit, we reveal that loyalty is
manifested in remarkably consistent behaviors across a wide spectrum of
communities. Loyal users employ language that signals collective identity and
engage with more esoteric, less popular content, indicating they may play a
curational role in surfacing new material. Loyal communities have denser
user-user interaction networks and lower rates of triadic closure, suggesting
that community-level loyalty is associated with more cohesive interactions and
less fragmentation into subgroups. We exploit these general patterns to predict
future rates of loyalty. Our results show that a user's propensity to become
loyal is apparent from their first interactions with a community, suggesting
that some users are intrinsically loyal from the very beginning.
| no_new_dataset | 0.943867 |
1703.03856 | Laurel Orr | Laurel Orr, Magda Balazinska, and Dan Suciu | Probabilistic Database Summarization for Interactive Data Exploration | To appear VLDB 2017 | null | null | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a probabilistic approach to generate a small, query-able summary
of a dataset for interactive data exploration. Departing from traditional
summarization techniques, we use the Principle of Maximum Entropy to generate a
probabilistic representation of the data that can be used to give approximate
query answers. We develop the theoretical framework and formulation of our
probabilistic representation and show how to use it to answer queries. We then
present solving techniques and give three critical optimizations to improve
preprocessing time and query accuracy. Lastly, we experimentally evaluate our
work using a 5 GB dataset of flights within the United States and a 210 GB
dataset from an astronomy particle simulation. While our current work only
supports linear queries, we show that our technique can successfully answer
queries faster than sampling while introducing, on average, no more error than
sampling and can better distinguish between rare and nonexistent values.
| [
{
"version": "v1",
"created": "Fri, 10 Mar 2017 22:17:22 GMT"
},
{
"version": "v2",
"created": "Tue, 23 May 2017 20:44:53 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Orr",
"Laurel",
""
],
[
"Balazinska",
"Magda",
""
],
[
"Suciu",
"Dan",
""
]
] | TITLE: Probabilistic Database Summarization for Interactive Data Exploration
ABSTRACT: We present a probabilistic approach to generate a small, query-able summary
of a dataset for interactive data exploration. Departing from traditional
summarization techniques, we use the Principle of Maximum Entropy to generate a
probabilistic representation of the data that can be used to give approximate
query answers. We develop the theoretical framework and formulation of our
probabilistic representation and show how to use it to answer queries. We then
present solving techniques and give three critical optimizations to improve
preprocessing time and query accuracy. Lastly, we experimentally evaluate our
work using a 5 GB dataset of flights within the United States and a 210 GB
dataset from an astronomy particle simulation. While our current work only
supports linear queries, we show that our technique can successfully answer
queries faster than sampling while introducing, on average, no more error than
sampling and can better distinguish between rare and nonexistent values.
| no_new_dataset | 0.92421 |
1705.05098 | Lahari Poddar | Lahari Poddar, Wynne Hsu, Mong Li Lee | Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach | Accepted for publication in IJCAI 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | User opinions expressed in the form of ratings can influence an individual's
view of an item. However, the true quality of an item is often obfuscated by
user biases, and it is not obvious from the observed ratings the importance
different users place on different aspects of an item. We propose a
probabilistic modeling of the observed aspect ratings to infer (i) each user's
aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect
ratings as ordered discrete data and encode the dependency between different
aspects by using a latent Gaussian structure. We handle the
Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled
with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully
Bayesian inference. On two real world datasets, we demonstrate the predictive
ability of our model and its effectiveness in learning explainable user biases
to provide insights towards a more reliable product quality estimation.
| [
{
"version": "v1",
"created": "Mon, 15 May 2017 07:35:59 GMT"
},
{
"version": "v2",
"created": "Wed, 24 May 2017 08:47:24 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Poddar",
"Lahari",
""
],
[
"Hsu",
"Wynne",
""
],
[
"Lee",
"Mong Li",
""
]
] | TITLE: Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach
ABSTRACT: User opinions expressed in the form of ratings can influence an individual's
view of an item. However, the true quality of an item is often obfuscated by
user biases, and it is not obvious from the observed ratings the importance
different users place on different aspects of an item. We propose a
probabilistic modeling of the observed aspect ratings to infer (i) each user's
aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect
ratings as ordered discrete data and encode the dependency between different
aspects by using a latent Gaussian structure. We handle the
Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled
with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully
Bayesian inference. On two real world datasets, we demonstrate the predictive
ability of our model and its effectiveness in learning explainable user biases
to provide insights towards a more reliable product quality estimation.
| no_new_dataset | 0.947284 |
1705.07425 | Thomas Niebler | Thomas Niebler, Martin Becker, Christian P\"olitz, Andreas Hotho | Learning Semantic Relatedness From Human Feedback Using Metric Learning | Under review at ISWC 2017 | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Assessing the degree of semantic relatedness between words is an important
task with a variety of semantic applications, such as ontology learning for the
Semantic Web, semantic search or query expansion. To accomplish this in an
automated fashion, many relatedness measures have been proposed. However, most
of these metrics only encode information contained in the underlying corpus and
thus do not directly model human intuition. To solve this, we propose to
utilize a metric learning approach to improve existing semantic relatedness
measures by learning from additional information, such as explicit human
feedback. For this, we argue to use word embeddings instead of traditional
high-dimensional vector representations in order to leverage their semantic
density and to reduce computational cost. We rigorously test our approach on
several domains including tagging data as well as publicly available embeddings
based on Wikipedia texts and navigation. Human feedback about semantic
relatedness for learning and evaluation is extracted from publicly available
datasets such as MEN or WS-353. We find that our method can significantly
improve semantic relatedness measures by learning from additional information,
such as explicit human feedback. For tagging data, we are the first to generate
and study embeddings. Our results are of special interest for ontology and
recommendation engineers, but also for any other researchers and practitioners
of Semantic Web techniques.
| [
{
"version": "v1",
"created": "Sun, 21 May 2017 10:16:49 GMT"
},
{
"version": "v2",
"created": "Wed, 24 May 2017 13:07:07 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Niebler",
"Thomas",
""
],
[
"Becker",
"Martin",
""
],
[
"Pölitz",
"Christian",
""
],
[
"Hotho",
"Andreas",
""
]
] | TITLE: Learning Semantic Relatedness From Human Feedback Using Metric Learning
ABSTRACT: Assessing the degree of semantic relatedness between words is an important
task with a variety of semantic applications, such as ontology learning for the
Semantic Web, semantic search or query expansion. To accomplish this in an
automated fashion, many relatedness measures have been proposed. However, most
of these metrics only encode information contained in the underlying corpus and
thus do not directly model human intuition. To solve this, we propose to
utilize a metric learning approach to improve existing semantic relatedness
measures by learning from additional information, such as explicit human
feedback. For this, we argue to use word embeddings instead of traditional
high-dimensional vector representations in order to leverage their semantic
density and to reduce computational cost. We rigorously test our approach on
several domains including tagging data as well as publicly available embeddings
based on Wikipedia texts and navigation. Human feedback about semantic
relatedness for learning and evaluation is extracted from publicly available
datasets such as MEN or WS-353. We find that our method can significantly
improve semantic relatedness measures by learning from additional information,
such as explicit human feedback. For tagging data, we are the first to generate
and study embeddings. Our results are of special interest for ontology and
recommendation engineers, but also for any other researchers and practitioners
of Semantic Web techniques.
| no_new_dataset | 0.951188 |
1705.08473 | Malay Chakrabarti | Malay Chakrabarti, Lenwood Heath, Naren Ramakrishnan | New methods to generate massive synthetic networks | null | null | null | null | cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the biggest needs in network science research is access to large
realistic datasets. As data analytics methods permeate a range of diverse
disciplines---e.g., computational epidemiology, sustainability, social media
analytics, biology, and transportation--- network datasets that can exhibit
characteristics encountered in each of these disciplines becomes paramount. The
key technical issue is to be able to generate synthetic topologies with
pre-specified, arbitrary, degree distributions. Existing methods are limited in
their ability to faithfully reproduce macro-level characteristics of networks
while at the same time respecting particular degree distributions. We present a
suite of three algorithms that exploit the principle of residual degree
attenuation to generate synthetic topologies that adhere to macro-level
real-world characteristics. By evaluating these algorithms w.r.t. several
real-world datasets we demonstrate their ability to faithfully reproduce
network characteristics such as node degree, clustering coefficient, hop
length, and k-core structure distributions.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 18:37:51 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Chakrabarti",
"Malay",
""
],
[
"Heath",
"Lenwood",
""
],
[
"Ramakrishnan",
"Naren",
""
]
] | TITLE: New methods to generate massive synthetic networks
ABSTRACT: One of the biggest needs in network science research is access to large
realistic datasets. As data analytics methods permeate a range of diverse
disciplines---e.g., computational epidemiology, sustainability, social media
analytics, biology, and transportation--- network datasets that can exhibit
characteristics encountered in each of these disciplines becomes paramount. The
key technical issue is to be able to generate synthetic topologies with
pre-specified, arbitrary, degree distributions. Existing methods are limited in
their ability to faithfully reproduce macro-level characteristics of networks
while at the same time respecting particular degree distributions. We present a
suite of three algorithms that exploit the principle of residual degree
attenuation to generate synthetic topologies that adhere to macro-level
real-world characteristics. By evaluating these algorithms w.r.t. several
real-world datasets we demonstrate their ability to faithfully reproduce
network characteristics such as node degree, clustering coefficient, hop
length, and k-core structure distributions.
| no_new_dataset | 0.947478 |
1705.08550 | Wentao Zhu | Wentao Zhu, Qi Lou, Yeeleng Scott Vang, and Xiaohui Xie | Deep Multi-instance Networks with Sparse Label Assignment for Whole
Mammogram Classification | MICCAI 2017 Camera Ready | null | null | null | cs.CV cs.LG cs.NE | http://creativecommons.org/licenses/by/4.0/ | Mammogram classification is directly related to computer-aided diagnosis of
breast cancer. Traditional methods rely on regions of interest (ROIs) which
require great efforts to annotate. Inspired by the success of using deep
convolutional features for natural image analysis and multi-instance learning
(MIL) for labeling a set of instances/patches, we propose end-to-end trained
deep multi-instance networks for mass classification based on whole mammogram
without the aforementioned ROIs. We explore three different schemes to
construct deep multi-instance networks for whole mammogram classification.
Experimental results on the INbreast dataset demonstrate the robustness of
proposed networks compared to previous work using segmentation and detection
annotations.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 22:16:20 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Zhu",
"Wentao",
""
],
[
"Lou",
"Qi",
""
],
[
"Vang",
"Yeeleng Scott",
""
],
[
"Xie",
"Xiaohui",
""
]
] | TITLE: Deep Multi-instance Networks with Sparse Label Assignment for Whole
Mammogram Classification
ABSTRACT: Mammogram classification is directly related to computer-aided diagnosis of
breast cancer. Traditional methods rely on regions of interest (ROIs) which
require great efforts to annotate. Inspired by the success of using deep
convolutional features for natural image analysis and multi-instance learning
(MIL) for labeling a set of instances/patches, we propose end-to-end trained
deep multi-instance networks for mass classification based on whole mammogram
without the aforementioned ROIs. We explore three different schemes to
construct deep multi-instance networks for whole mammogram classification.
Experimental results on the INbreast dataset demonstrate the robustness of
proposed networks compared to previous work using segmentation and detection
annotations.
| no_new_dataset | 0.952042 |
1705.08557 | Ankit Vani | Ankit Vani, Yacine Jernite, David Sontag | Grounded Recurrent Neural Networks | null | null | null | null | stat.ML cs.CL cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we present the Grounded Recurrent Neural Network (GRNN), a
recurrent neural network architecture for multi-label prediction which
explicitly ties labels to specific dimensions of the recurrent hidden state (we
call this process "grounding"). The approach is particularly well-suited for
extracting large numbers of concepts from text. We apply the new model to
address an important problem in healthcare of understanding what medical
concepts are discussed in clinical text. Using a publicly available dataset
derived from Intensive Care Units, we learn to label a patient's diagnoses and
procedures from their discharge summary. Our evaluation shows a clear advantage
to using our proposed architecture over a variety of strong baselines.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 23:17:49 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Vani",
"Ankit",
""
],
[
"Jernite",
"Yacine",
""
],
[
"Sontag",
"David",
""
]
] | TITLE: Grounded Recurrent Neural Networks
ABSTRACT: In this work, we present the Grounded Recurrent Neural Network (GRNN), a
recurrent neural network architecture for multi-label prediction which
explicitly ties labels to specific dimensions of the recurrent hidden state (we
call this process "grounding"). The approach is particularly well-suited for
extracting large numbers of concepts from text. We apply the new model to
address an important problem in healthcare of understanding what medical
concepts are discussed in clinical text. Using a publicly available dataset
derived from Intensive Care Units, we learn to label a patient's diagnoses and
procedures from their discharge summary. Our evaluation shows a clear advantage
to using our proposed architecture over a variety of strong baselines.
| no_new_dataset | 0.954223 |
1705.08583 | Anoop Cherian | Anoop Cherian, Suvrit Sra, Richard Hartley | Sequence Summarization Using Order-constrained Kernelized Feature
Subspaces | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Representations that can compactly and effectively capture temporal evolution
of semantic content are important to machine learning algorithms that operate
on multi-variate time-series data. We investigate such representations
motivated by the task of human action recognition. Here each data instance is
encoded by a multivariate feature (such as via a deep CNN) where action
dynamics are characterized by their variations in time. As these features are
often non-linear, we propose a novel pooling method, kernelized rank pooling,
that represents a given sequence compactly as the pre-image of the parameters
of a hyperplane in an RKHS, projections of data onto which captures their
temporal order. We develop this idea further and show that such a pooling
scheme can be cast as an order-constrained kernelized PCA objective; we then
propose to use the parameters of a kernelized low-rank feature subspace as the
representation of the sequences. We cast our formulation as an optimization
problem on generalized Grassmann manifolds and then solve it efficiently using
Riemannian optimization techniques. We present experiments on several action
recognition datasets using diverse feature modalities and demonstrate
state-of-the-art results.
| [
{
"version": "v1",
"created": "Wed, 24 May 2017 02:11:04 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Cherian",
"Anoop",
""
],
[
"Sra",
"Suvrit",
""
],
[
"Hartley",
"Richard",
""
]
] | TITLE: Sequence Summarization Using Order-constrained Kernelized Feature
Subspaces
ABSTRACT: Representations that can compactly and effectively capture temporal evolution
of semantic content are important to machine learning algorithms that operate
on multi-variate time-series data. We investigate such representations
motivated by the task of human action recognition. Here each data instance is
encoded by a multivariate feature (such as via a deep CNN) where action
dynamics are characterized by their variations in time. As these features are
often non-linear, we propose a novel pooling method, kernelized rank pooling,
that represents a given sequence compactly as the pre-image of the parameters
of a hyperplane in an RKHS, projections of data onto which captures their
temporal order. We develop this idea further and show that such a pooling
scheme can be cast as an order-constrained kernelized PCA objective; we then
propose to use the parameters of a kernelized low-rank feature subspace as the
representation of the sequences. We cast our formulation as an optimization
problem on generalized Grassmann manifolds and then solve it efficiently using
Riemannian optimization techniques. We present experiments on several action
recognition datasets using diverse feature modalities and demonstrate
state-of-the-art results.
| no_new_dataset | 0.947624 |
1705.08593 | Davit Buniatyan | Davit Buniatyan, Thomas Macrina, Dodam Ih, Jonathan Zung, H. Sebastian
Seung | Deep Learning Improves Template Matching by Normalized Cross Correlation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Template matching by normalized cross correlation (NCC) is widely used for
finding image correspondences. We improve the robustness of this algorithm by
preprocessing images with "siamese" convolutional networks trained to maximize
the contrast between NCC values of true and false matches. The improvement is
quantified using patches of brain images from serial section electron
microscopy. Relative to a parameter-tuned bandpass filter, siamese
convolutional networks significantly reduce false matches. Furthermore, all
false matches can be eliminated by removing a tiny fraction of all matches
based on NCC values. The improved accuracy of our method could be essential for
connectomics, because emerging petascale datasets may require billions of
template matches to assemble 2D images of serial sections into a 3D image
stack. Our method is also expected to generalize to many other computer vision
applications that use NCC template matching to find image correspondences.
| [
{
"version": "v1",
"created": "Wed, 24 May 2017 03:24:25 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Buniatyan",
"Davit",
""
],
[
"Macrina",
"Thomas",
""
],
[
"Ih",
"Dodam",
""
],
[
"Zung",
"Jonathan",
""
],
[
"Seung",
"H. Sebastian",
""
]
] | TITLE: Deep Learning Improves Template Matching by Normalized Cross Correlation
ABSTRACT: Template matching by normalized cross correlation (NCC) is widely used for
finding image correspondences. We improve the robustness of this algorithm by
preprocessing images with "siamese" convolutional networks trained to maximize
the contrast between NCC values of true and false matches. The improvement is
quantified using patches of brain images from serial section electron
microscopy. Relative to a parameter-tuned bandpass filter, siamese
convolutional networks significantly reduce false matches. Furthermore, all
false matches can be eliminated by removing a tiny fraction of all matches
based on NCC values. The improved accuracy of our method could be essential for
connectomics, because emerging petascale datasets may require billions of
template matches to assemble 2D images of serial sections into a 3D image
stack. Our method is also expected to generalize to many other computer vision
applications that use NCC template matching to find image correspondences.
| no_new_dataset | 0.949995 |
1705.08631 | Lluis Gomez | Lluis Gomez, Yash Patel, Mar\c{c}al Rusi\~nol, Dimosthenis Karatzas,
C.V. Jawahar | Self-supervised learning of visual features through embedding images
into text topic spaces | Accepted CVPR 2017 paper | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | End-to-end training from scratch of current deep architectures for new
computer vision problems would require Imagenet-scale datasets, and this is not
always possible. In this paper we present a method that is able to take
advantage of freely available multi-modal content to train computer vision
algorithms without human supervision. We put forward the idea of performing
self-supervised learning of visual features by mining a large scale corpus of
multi-modal (text and image) documents. We show that discriminative visual
features can be learnt efficiently by training a CNN to predict the semantic
context in which a particular image is more probable to appear as an
illustration. For this we leverage the hidden semantic structures discovered in
the text corpus with a well-known topic modeling technique. Our experiments
demonstrate state of the art performance in image classification, object
detection, and multi-modal retrieval compared to recent self-supervised or
natural-supervised approaches.
| [
{
"version": "v1",
"created": "Wed, 24 May 2017 06:59:30 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Gomez",
"Lluis",
""
],
[
"Patel",
"Yash",
""
],
[
"Rusiñol",
"Marçal",
""
],
[
"Karatzas",
"Dimosthenis",
""
],
[
"Jawahar",
"C. V.",
""
]
] | TITLE: Self-supervised learning of visual features through embedding images
into text topic spaces
ABSTRACT: End-to-end training from scratch of current deep architectures for new
computer vision problems would require Imagenet-scale datasets, and this is not
always possible. In this paper we present a method that is able to take
advantage of freely available multi-modal content to train computer vision
algorithms without human supervision. We put forward the idea of performing
self-supervised learning of visual features by mining a large scale corpus of
multi-modal (text and image) documents. We show that discriminative visual
features can be learnt efficiently by training a CNN to predict the semantic
context in which a particular image is more probable to appear as an
illustration. For this we leverage the hidden semantic structures discovered in
the text corpus with a well-known topic modeling technique. Our experiments
demonstrate state of the art performance in image classification, object
detection, and multi-modal retrieval compared to recent self-supervised or
natural-supervised approaches.
| no_new_dataset | 0.942665 |
1705.08759 | Stefan Lee | Qing Sun, Stefan Lee, Dhruv Batra | Bidirectional Beam Search: Forward-Backward Inference in Neural Sequence
Models for Fill-in-the-Blank Image Captioning | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop the first approximate inference algorithm for 1-Best (and M-Best)
decoding in bidirectional neural sequence models by extending Beam Search (BS)
to reason about both forward and backward time dependencies. Beam Search (BS)
is a widely used approximate inference algorithm for decoding sequences from
unidirectional neural sequence models. Interestingly, approximate inference in
bidirectional models remains an open problem, despite their significant
advantage in modeling information from both the past and future. To enable the
use of bidirectional models, we present Bidirectional Beam Search (BiBS), an
efficient algorithm for approximate bidirectional inference.To evaluate our
method and as an interesting problem in its own right, we introduce a novel
Fill-in-the-Blank Image Captioning task which requires reasoning about both
past and future sentence structure to reconstruct sensible image descriptions.
We use this task as well as the Visual Madlibs dataset to demonstrate the
effectiveness of our approach, consistently outperforming all baseline methods.
| [
{
"version": "v1",
"created": "Wed, 24 May 2017 13:42:47 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Sun",
"Qing",
""
],
[
"Lee",
"Stefan",
""
],
[
"Batra",
"Dhruv",
""
]
] | TITLE: Bidirectional Beam Search: Forward-Backward Inference in Neural Sequence
Models for Fill-in-the-Blank Image Captioning
ABSTRACT: We develop the first approximate inference algorithm for 1-Best (and M-Best)
decoding in bidirectional neural sequence models by extending Beam Search (BS)
to reason about both forward and backward time dependencies. Beam Search (BS)
is a widely used approximate inference algorithm for decoding sequences from
unidirectional neural sequence models. Interestingly, approximate inference in
bidirectional models remains an open problem, despite their significant
advantage in modeling information from both the past and future. To enable the
use of bidirectional models, we present Bidirectional Beam Search (BiBS), an
efficient algorithm for approximate bidirectional inference.To evaluate our
method and as an interesting problem in its own right, we introduce a novel
Fill-in-the-Blank Image Captioning task which requires reasoning about both
past and future sentence structure to reconstruct sensible image descriptions.
We use this task as well as the Visual Madlibs dataset to demonstrate the
effectiveness of our approach, consistently outperforming all baseline methods.
| no_new_dataset | 0.944074 |
1705.08828 | Laurent Besacier | Jeremy Ferrero, Laurent Besacier, Didier Schwab and Frederic Agnes | Deep Investigation of Cross-Language Plagiarism Detection Methods | Accepted to BUCC (10th Workshop on Building and Using Comparable
Corpora) colocated with ACL 2017 | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | This paper is a deep investigation of cross-language plagiarism detection
methods on a new recently introduced open dataset, which contains parallel and
comparable collections of documents with multiple characteristics (different
genres, languages and sizes of texts). We investigate cross-language plagiarism
detection methods for 6 language pairs on 2 granularities of text units in
order to draw robust conclusions on the best methods while deeply analyzing
correlations across document styles and languages.
| [
{
"version": "v1",
"created": "Wed, 24 May 2017 15:50:47 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Ferrero",
"Jeremy",
""
],
[
"Besacier",
"Laurent",
""
],
[
"Schwab",
"Didier",
""
],
[
"Agnes",
"Frederic",
""
]
] | TITLE: Deep Investigation of Cross-Language Plagiarism Detection Methods
ABSTRACT: This paper is a deep investigation of cross-language plagiarism detection
methods on a new recently introduced open dataset, which contains parallel and
comparable collections of documents with multiple characteristics (different
genres, languages and sizes of texts). We investigate cross-language plagiarism
detection methods for 6 language pairs on 2 granularities of text units in
order to draw robust conclusions on the best methods while deeply analyzing
correlations across document styles and languages.
| new_dataset | 0.948822 |
1705.08844 | Rodrigo Toro Icarte | Rodrigo Toro Icarte, Jorge A. Baier, Cristian Ruz, Alvaro Soto | How a General-Purpose Commonsense Ontology can Improve Performance of
Learning-Based Image Retrieval | Accepted in IJCAI-17 | null | null | null | cs.AI cs.CV cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The knowledge representation community has built general-purpose ontologies
which contain large amounts of commonsense knowledge over relevant aspects of
the world, including useful visual information, e.g.: "a ball is used by a
football player", "a tennis player is located at a tennis court". Current
state-of-the-art approaches for visual recognition do not exploit these
rule-based knowledge sources. Instead, they learn recognition models directly
from training examples. In this paper, we study how general-purpose
ontologies---specifically, MIT's ConceptNet ontology---can improve the
performance of state-of-the-art vision systems. As a testbed, we tackle the
problem of sentence-based image retrieval. Our retrieval approach incorporates
knowledge from ConceptNet on top of a large pool of object detectors derived
from a deep learning technique. In our experiments, we show that ConceptNet can
improve performance on a common benchmark dataset. Key to our performance is
the use of the ESPGAME dataset to select visually relevant relations from
ConceptNet. Consequently, a main conclusion of this work is that
general-purpose commonsense ontologies improve performance on visual reasoning
tasks when properly filtered to select meaningful visual relations.
| [
{
"version": "v1",
"created": "Wed, 24 May 2017 16:22:53 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Icarte",
"Rodrigo Toro",
""
],
[
"Baier",
"Jorge A.",
""
],
[
"Ruz",
"Cristian",
""
],
[
"Soto",
"Alvaro",
""
]
] | TITLE: How a General-Purpose Commonsense Ontology can Improve Performance of
Learning-Based Image Retrieval
ABSTRACT: The knowledge representation community has built general-purpose ontologies
which contain large amounts of commonsense knowledge over relevant aspects of
the world, including useful visual information, e.g.: "a ball is used by a
football player", "a tennis player is located at a tennis court". Current
state-of-the-art approaches for visual recognition do not exploit these
rule-based knowledge sources. Instead, they learn recognition models directly
from training examples. In this paper, we study how general-purpose
ontologies---specifically, MIT's ConceptNet ontology---can improve the
performance of state-of-the-art vision systems. As a testbed, we tackle the
problem of sentence-based image retrieval. Our retrieval approach incorporates
knowledge from ConceptNet on top of a large pool of object detectors derived
from a deep learning technique. In our experiments, we show that ConceptNet can
improve performance on a common benchmark dataset. Key to our performance is
the use of the ESPGAME dataset to select visually relevant relations from
ConceptNet. Consequently, a main conclusion of this work is that
general-purpose commonsense ontologies improve performance on visual reasoning
tasks when properly filtered to select meaningful visual relations.
| no_new_dataset | 0.945399 |
1705.08858 | Galina Lavrentyeva | Galina Lavrentyeva, Sergey Novoselov, Egor Malykh, Alexander Kozlov,
Oleg Kudashev and Vadim Shchemelinin | Audio-replay attack detection countermeasures | 11 pages, 3 figures, accepted for Specom 2017 | null | null | null | cs.SD cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents the Speech Technology Center (STC) replay attack
detection systems proposed for Automatic Speaker Verification Spoofing and
Countermeasures Challenge 2017. In this study we focused on comparison of
different spoofing detection approaches. These were GMM based methods, high
level features extraction with simple classifier and deep learning frameworks.
Experiments performed on the development and evaluation parts of the challenge
dataset demonstrated stable efficiency of deep learning approaches in case of
changing acoustic conditions. At the same time SVM classifier with high level
features provided a substantial input in the efficiency of the resulting STC
systems according to the fusion systems results.
| [
{
"version": "v1",
"created": "Wed, 24 May 2017 16:48:03 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Lavrentyeva",
"Galina",
""
],
[
"Novoselov",
"Sergey",
""
],
[
"Malykh",
"Egor",
""
],
[
"Kozlov",
"Alexander",
""
],
[
"Kudashev",
"Oleg",
""
],
[
"Shchemelinin",
"Vadim",
""
]
] | TITLE: Audio-replay attack detection countermeasures
ABSTRACT: This paper presents the Speech Technology Center (STC) replay attack
detection systems proposed for Automatic Speaker Verification Spoofing and
Countermeasures Challenge 2017. In this study we focused on comparison of
different spoofing detection approaches. These were GMM based methods, high
level features extraction with simple classifier and deep learning frameworks.
Experiments performed on the development and evaluation parts of the challenge
dataset demonstrated stable efficiency of deep learning approaches in case of
changing acoustic conditions. At the same time SVM classifier with high level
features provided a substantial input in the efficiency of the resulting STC
systems according to the fusion systems results.
| no_new_dataset | 0.94428 |
1705.08865 | Galina Lavrentyeva | Galina Lavrentyeva, Sergey Novoselov and Konstantin Simonchik | Anti-spoofing Methods for Automatic SpeakerVerification System | 12 pages, 0 figures, published in Springer Communications in Computer
and Information Science (CCIS) vol. 661 | null | null | null | cs.SD cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Growing interest in automatic speaker verification (ASV)systems has lead to
significant quality improvement of spoofing attackson them. Many research works
confirm that despite the low equal er-ror rate (EER) ASV systems are still
vulnerable to spoofing attacks. Inthis work we overview different acoustic
feature spaces and classifiersto determine reliable and robust countermeasures
against spoofing at-tacks. We compared several spoofing detection systems,
presented so far,on the development and evaluation datasets of the Automatic
SpeakerVerification Spoofing and Countermeasures (ASVspoof) Challenge
2015.Experimental results presented in this paper demonstrate that the useof
magnitude and phase information combination provides a substantialinput into
the efficiency of the spoofing detection systems. Also wavelet-based features
show impressive results in terms of equal error rate. Inour overview we compare
spoofing performance for systems based on dif-ferent classifiers. Comparison
results demonstrate that the linear SVMclassifier outperforms the conventional
GMM approach. However, manyresearchers inspired by the great success of deep
neural networks (DNN)approaches in the automatic speech recognition, applied
DNN in thespoofing detection task and obtained quite low EER for known and
un-known type of spoofing attacks.
| [
{
"version": "v1",
"created": "Wed, 24 May 2017 16:58:03 GMT"
}
] | 2017-05-25T00:00:00 | [
[
"Lavrentyeva",
"Galina",
""
],
[
"Novoselov",
"Sergey",
""
],
[
"Simonchik",
"Konstantin",
""
]
] | TITLE: Anti-spoofing Methods for Automatic SpeakerVerification System
ABSTRACT: Growing interest in automatic speaker verification (ASV)systems has lead to
significant quality improvement of spoofing attackson them. Many research works
confirm that despite the low equal er-ror rate (EER) ASV systems are still
vulnerable to spoofing attacks. Inthis work we overview different acoustic
feature spaces and classifiersto determine reliable and robust countermeasures
against spoofing at-tacks. We compared several spoofing detection systems,
presented so far,on the development and evaluation datasets of the Automatic
SpeakerVerification Spoofing and Countermeasures (ASVspoof) Challenge
2015.Experimental results presented in this paper demonstrate that the useof
magnitude and phase information combination provides a substantialinput into
the efficiency of the spoofing detection systems. Also wavelet-based features
show impressive results in terms of equal error rate. Inour overview we compare
spoofing performance for systems based on dif-ferent classifiers. Comparison
results demonstrate that the linear SVMclassifier outperforms the conventional
GMM approach. However, manyresearchers inspired by the great success of deep
neural networks (DNN)approaches in the automatic speech recognition, applied
DNN in thespoofing detection task and obtained quite low EER for known and
un-known type of spoofing attacks.
| no_new_dataset | 0.940898 |
1601.02522 | Nathanael Perraudin N. P. | Nathana\"el Perraudin, Pierre Vandergheynst | Stationary signal processing on graphs | null | null | 10.1109/TSP.2017.2690388 | null | cs.DS stat.AP stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graphs are a central tool in machine learning and information processing as
they allow to conveniently capture the structure of complex datasets. In this
context, it is of high importance to develop flexible models of signals defined
over graphs or networks. In this paper, we generalize the traditional concept
of wide sense stationarity to signals defined over the vertices of arbitrary
weighted undirected graphs. We show that stationarity is expressed through the
graph localization operator reminiscent of translation. We prove that
stationary graph signals are characterized by a well-defined Power Spectral
Density that can be efficiently estimated even for large graphs. We leverage
this new concept to derive Wiener-type estimation procedures of noisy and
partially observed signals and illustrate the performance of this new model for
denoising and regression.
| [
{
"version": "v1",
"created": "Mon, 11 Jan 2016 16:58:45 GMT"
},
{
"version": "v2",
"created": "Tue, 12 Jan 2016 16:42:30 GMT"
},
{
"version": "v3",
"created": "Thu, 21 Apr 2016 16:34:34 GMT"
},
{
"version": "v4",
"created": "Fri, 8 Jul 2016 21:25:26 GMT"
},
{
"version": "v5",
"created": "Fri, 21 Apr 2017 18:30:15 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Perraudin",
"Nathanaël",
""
],
[
"Vandergheynst",
"Pierre",
""
]
] | TITLE: Stationary signal processing on graphs
ABSTRACT: Graphs are a central tool in machine learning and information processing as
they allow to conveniently capture the structure of complex datasets. In this
context, it is of high importance to develop flexible models of signals defined
over graphs or networks. In this paper, we generalize the traditional concept
of wide sense stationarity to signals defined over the vertices of arbitrary
weighted undirected graphs. We show that stationarity is expressed through the
graph localization operator reminiscent of translation. We prove that
stationary graph signals are characterized by a well-defined Power Spectral
Density that can be efficiently estimated even for large graphs. We leverage
this new concept to derive Wiener-type estimation procedures of noisy and
partially observed signals and illustrate the performance of this new model for
denoising and regression.
| no_new_dataset | 0.950686 |
1602.02885 | Yeejin Lee | Y.J. Lee, K. Hirakawa, and T.Q. Nguyen | Joint Defogging and Demosaicking | null | null | 10.1109/TIP.2016.2631880 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Image defogging is a technique used extensively for enhancing visual quality
of images in bad weather condition. Even though defogging algorithms have been
well studied, defogging performance is degraded by demosaicking artifacts and
sensor noise amplification in distant scenes. In order to improve visual
quality of restored images, we propose a novel approach to perform defogging
and demosaicking simultaneously. We conclude that better defogging performance
with fewer artifacts can be achieved when a defogging algorithm is combined
with a demosaicking algorithm simultaneously. We also demonstrate that the
proposed joint algorithm has the benefit of suppressing noise amplification in
distant scene. In addition, we validate our theoretical analysis and
observations for both synthesized datasets with ground truth fog-free images
and natural scene datasets captured in a raw format.
| [
{
"version": "v1",
"created": "Tue, 9 Feb 2016 08:01:20 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Lee",
"Y. J.",
""
],
[
"Hirakawa",
"K.",
""
],
[
"Nguyen",
"T. Q.",
""
]
] | TITLE: Joint Defogging and Demosaicking
ABSTRACT: Image defogging is a technique used extensively for enhancing visual quality
of images in bad weather condition. Even though defogging algorithms have been
well studied, defogging performance is degraded by demosaicking artifacts and
sensor noise amplification in distant scenes. In order to improve visual
quality of restored images, we propose a novel approach to perform defogging
and demosaicking simultaneously. We conclude that better defogging performance
with fewer artifacts can be achieved when a defogging algorithm is combined
with a demosaicking algorithm simultaneously. We also demonstrate that the
proposed joint algorithm has the benefit of suppressing noise amplification in
distant scene. In addition, we validate our theoretical analysis and
observations for both synthesized datasets with ground truth fog-free images
and natural scene datasets captured in a raw format.
| no_new_dataset | 0.949995 |
1606.02009 | Salman Khan Mr. | Salman H Khan, Xuming He, Fatih Porikli, Mohammed Bennamoun, Ferdous
Sohel, Roberto Togneri | Learning deep structured network for weakly supervised change detection | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conventional change detection methods require a large number of images to
learn background models or depend on tedious pixel-level labeling by humans. In
this paper, we present a weakly supervised approach that needs only image-level
labels to simultaneously detect and localize changes in a pair of images. To
this end, we employ a deep neural network with DAG topology to learn patterns
of change from image-level labeled training data. On top of the initial CNN
activations, we define a CRF model to incorporate the local differences and
context with the dense connections between individual pixels. We apply a
constrained mean-field algorithm to estimate the pixel-level labels, and use
the estimated labels to update the parameters of the CNN in an iterative EM
framework. This enables imposing global constraints on the observed foreground
probability mass function. Our evaluations on four benchmark datasets
demonstrate superior detection and localization performance.
| [
{
"version": "v1",
"created": "Tue, 7 Jun 2016 03:20:37 GMT"
},
{
"version": "v2",
"created": "Tue, 23 May 2017 01:22:06 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Khan",
"Salman H",
""
],
[
"He",
"Xuming",
""
],
[
"Porikli",
"Fatih",
""
],
[
"Bennamoun",
"Mohammed",
""
],
[
"Sohel",
"Ferdous",
""
],
[
"Togneri",
"Roberto",
""
]
] | TITLE: Learning deep structured network for weakly supervised change detection
ABSTRACT: Conventional change detection methods require a large number of images to
learn background models or depend on tedious pixel-level labeling by humans. In
this paper, we present a weakly supervised approach that needs only image-level
labels to simultaneously detect and localize changes in a pair of images. To
this end, we employ a deep neural network with DAG topology to learn patterns
of change from image-level labeled training data. On top of the initial CNN
activations, we define a CRF model to incorporate the local differences and
context with the dense connections between individual pixels. We apply a
constrained mean-field algorithm to estimate the pixel-level labels, and use
the estimated labels to update the parameters of the CNN in an iterative EM
framework. This enables imposing global constraints on the observed foreground
probability mass function. Our evaluations on four benchmark datasets
demonstrate superior detection and localization performance.
| no_new_dataset | 0.952397 |
1608.01807 | Liang Zheng | Liang Zheng, Yi Yang, Qi Tian | SIFT Meets CNN: A Decade Survey of Instance Retrieval | Accepted to IEEE Transactions on Pattern Analysis and Machine
Intelligence | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the early days, content-based image retrieval (CBIR) was studied with
global features. Since 2003, image retrieval based on local descriptors (de
facto SIFT) has been extensively studied for over a decade due to the advantage
of SIFT in dealing with image transformations. Recently, image representations
based on the convolutional neural network (CNN) have attracted increasing
interest in the community and demonstrated impressive performance. Given this
time of rapid evolution, this article provides a comprehensive survey of
instance retrieval over the last decade. Two broad categories, SIFT-based and
CNN-based methods, are presented. For the former, according to the codebook
size, we organize the literature into using large/medium-sized/small codebooks.
For the latter, we discuss three lines of methods, i.e., using pre-trained or
fine-tuned CNN models, and hybrid methods. The first two perform a single-pass
of an image to the network, while the last category employs a patch-based
feature extraction scheme. This survey presents milestones in modern instance
retrieval, reviews a broad selection of previous works in different categories,
and provides insights on the connection between SIFT and CNN-based methods.
After analyzing and comparing retrieval performance of different categories on
several datasets, we discuss promising directions towards generic and
specialized instance retrieval.
| [
{
"version": "v1",
"created": "Fri, 5 Aug 2016 08:50:58 GMT"
},
{
"version": "v2",
"created": "Tue, 23 May 2017 08:10:33 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Zheng",
"Liang",
""
],
[
"Yang",
"Yi",
""
],
[
"Tian",
"Qi",
""
]
] | TITLE: SIFT Meets CNN: A Decade Survey of Instance Retrieval
ABSTRACT: In the early days, content-based image retrieval (CBIR) was studied with
global features. Since 2003, image retrieval based on local descriptors (de
facto SIFT) has been extensively studied for over a decade due to the advantage
of SIFT in dealing with image transformations. Recently, image representations
based on the convolutional neural network (CNN) have attracted increasing
interest in the community and demonstrated impressive performance. Given this
time of rapid evolution, this article provides a comprehensive survey of
instance retrieval over the last decade. Two broad categories, SIFT-based and
CNN-based methods, are presented. For the former, according to the codebook
size, we organize the literature into using large/medium-sized/small codebooks.
For the latter, we discuss three lines of methods, i.e., using pre-trained or
fine-tuned CNN models, and hybrid methods. The first two perform a single-pass
of an image to the network, while the last category employs a patch-based
feature extraction scheme. This survey presents milestones in modern instance
retrieval, reviews a broad selection of previous works in different categories,
and provides insights on the connection between SIFT and CNN-based methods.
After analyzing and comparing retrieval performance of different categories on
several datasets, we discuss promising directions towards generic and
specialized instance retrieval.
| no_new_dataset | 0.945851 |
1610.07986 | Thomas Kreuz | Thomas Kreuz, Eero Satuvuori, Martin Pofahl, Mario Mulansky | Leaders and followers: Quantifying consistency in spatio-temporal
propagation patterns | 18 pages; 18 figures; revised version | null | 10.1088/1367-2630/aa68c3 | null | physics.data-an q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Repetitive spatio-temporal propagation patterns are encountered in fields as
wide-ranging as climatology, social communication and network science. In
neuroscience, perfectly consistent repetitions of the same global propagation
pattern are called a synfire pattern. For any recording of sequences of
discrete events (in neuroscience terminology: sets of spike trains) the
questions arise how closely it resembles such a synfire pattern and which are
the spike trains that lead/follow. Here we address these questions and
introduce an algorithm built on two new indicators, termed SPIKE-Order and
Spike Train Order, that define the Synfire Indicator value, which allows to
sort multiple spike trains from leader to follower and to quantify the
consistency of the temporal leader-follower relationships for both the original
and the optimized sorting. We demonstrate our new approach using artificially
generated datasets before we apply it to analyze the consistency of propagation
patterns in two real datasets from neuroscience (Giant Depolarized Potentials
in mice slices) and climatology (El Ni~no sea surface temperature recordings).
The new algorithm is distinguished by conceptual and practical simplicity, low
computational cost, as well as flexibility and universality.
| [
{
"version": "v1",
"created": "Tue, 25 Oct 2016 17:51:23 GMT"
},
{
"version": "v2",
"created": "Wed, 26 Oct 2016 08:24:50 GMT"
},
{
"version": "v3",
"created": "Mon, 13 Feb 2017 21:36:09 GMT"
},
{
"version": "v4",
"created": "Wed, 22 Mar 2017 15:26:05 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Kreuz",
"Thomas",
""
],
[
"Satuvuori",
"Eero",
""
],
[
"Pofahl",
"Martin",
""
],
[
"Mulansky",
"Mario",
""
]
] | TITLE: Leaders and followers: Quantifying consistency in spatio-temporal
propagation patterns
ABSTRACT: Repetitive spatio-temporal propagation patterns are encountered in fields as
wide-ranging as climatology, social communication and network science. In
neuroscience, perfectly consistent repetitions of the same global propagation
pattern are called a synfire pattern. For any recording of sequences of
discrete events (in neuroscience terminology: sets of spike trains) the
questions arise how closely it resembles such a synfire pattern and which are
the spike trains that lead/follow. Here we address these questions and
introduce an algorithm built on two new indicators, termed SPIKE-Order and
Spike Train Order, that define the Synfire Indicator value, which allows to
sort multiple spike trains from leader to follower and to quantify the
consistency of the temporal leader-follower relationships for both the original
and the optimized sorting. We demonstrate our new approach using artificially
generated datasets before we apply it to analyze the consistency of propagation
patterns in two real datasets from neuroscience (Giant Depolarized Potentials
in mice slices) and climatology (El Ni~no sea surface temperature recordings).
The new algorithm is distinguished by conceptual and practical simplicity, low
computational cost, as well as flexibility and universality.
| no_new_dataset | 0.935051 |
1611.00812 | Jiemin Chen | Jianguo Li, Yong Tang and Jiemin Chen | Leveraging tagging and rating for recommendation: RMF meets weighted
diffusion on tripartite graphs | null | null | 10.1016/j.physa.2017.04.121 | null | cs.IR cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recommender systems (RSs) have been a widely exploited approach to solving
the information overload problem. However, the performance is still limited due
to the extreme sparsity of the rating data. With the popularity of Web 2.0, the
social tagging system provides more external information to improve
recommendation accuracy. Although some existing approaches combine the matrix
factorization models with co-occurrence properties and context of tags, they
neglect the issue of tag sparsity without the commonly associated tags problem
that would also result in inaccurate recommendations. Consequently, in this
paper, we propose a novel hybrid collaborative filtering model named
WUDiff_RMF, which improves Regularized Matrix Factorization (RMF) model by
integrating Weighted User-Diffusion-based CF algorithm(WUDiff) that obtains the
information of similar users from the weighted tripartite user-item-tag graph.
This model aims to capture the degree correlation of the user-item-tag
tripartite network to enhance the performance of recommendation. Experiments
conducted on four real-world datasets demonstrate that our approach
significantly performs better than already widely used methods in the accuracy
of recommendation. Moreover, results show that WUDiff_RMF can alleviate the
data sparsity, especially in the circumstance that users have made few ratings
and few tags.
| [
{
"version": "v1",
"created": "Tue, 1 Nov 2016 02:59:04 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Li",
"Jianguo",
""
],
[
"Tang",
"Yong",
""
],
[
"Chen",
"Jiemin",
""
]
] | TITLE: Leveraging tagging and rating for recommendation: RMF meets weighted
diffusion on tripartite graphs
ABSTRACT: Recommender systems (RSs) have been a widely exploited approach to solving
the information overload problem. However, the performance is still limited due
to the extreme sparsity of the rating data. With the popularity of Web 2.0, the
social tagging system provides more external information to improve
recommendation accuracy. Although some existing approaches combine the matrix
factorization models with co-occurrence properties and context of tags, they
neglect the issue of tag sparsity without the commonly associated tags problem
that would also result in inaccurate recommendations. Consequently, in this
paper, we propose a novel hybrid collaborative filtering model named
WUDiff_RMF, which improves Regularized Matrix Factorization (RMF) model by
integrating Weighted User-Diffusion-based CF algorithm(WUDiff) that obtains the
information of similar users from the weighted tripartite user-item-tag graph.
This model aims to capture the degree correlation of the user-item-tag
tripartite network to enhance the performance of recommendation. Experiments
conducted on four real-world datasets demonstrate that our approach
significantly performs better than already widely used methods in the accuracy
of recommendation. Moreover, results show that WUDiff_RMF can alleviate the
data sparsity, especially in the circumstance that users have made few ratings
and few tags.
| no_new_dataset | 0.948822 |
1701.08349 | Xiaoxia Sun | Xiaoxia Sun, Nasser M. Nasrabadi, Trac D. Tran | Supervised Deep Sparse Coding Networks | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we describe the deep sparse coding network (SCN), a novel deep
network that encodes intermediate representations with nonnegative sparse
coding. The SCN is built upon a number of cascading bottleneck modules, where
each module consists of two sparse coding layers with relatively wide and slim
dictionaries that are specialized to produce high dimensional discriminative
features and low dimensional representations for clustering, respectively.
During training, both the dictionaries and regularization parameters are
optimized with an end-to-end supervised learning algorithm based on multilevel
optimization. Effectiveness of an SCN with seven bottleneck modules is verified
on several popular benchmark datasets. Remarkably, with few parameters to
learn, our SCN achieves 5.81% and 19.93% classification error rate on CIFAR-10
and CIFAR-100, respectively.
| [
{
"version": "v1",
"created": "Sun, 29 Jan 2017 04:03:39 GMT"
},
{
"version": "v2",
"created": "Sun, 21 May 2017 01:33:04 GMT"
},
{
"version": "v3",
"created": "Tue, 23 May 2017 01:31:04 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Sun",
"Xiaoxia",
""
],
[
"Nasrabadi",
"Nasser M.",
""
],
[
"Tran",
"Trac D.",
""
]
] | TITLE: Supervised Deep Sparse Coding Networks
ABSTRACT: In this paper, we describe the deep sparse coding network (SCN), a novel deep
network that encodes intermediate representations with nonnegative sparse
coding. The SCN is built upon a number of cascading bottleneck modules, where
each module consists of two sparse coding layers with relatively wide and slim
dictionaries that are specialized to produce high dimensional discriminative
features and low dimensional representations for clustering, respectively.
During training, both the dictionaries and regularization parameters are
optimized with an end-to-end supervised learning algorithm based on multilevel
optimization. Effectiveness of an SCN with seven bottleneck modules is verified
on several popular benchmark datasets. Remarkably, with few parameters to
learn, our SCN achieves 5.81% and 19.93% classification error rate on CIFAR-10
and CIFAR-100, respectively.
| no_new_dataset | 0.948489 |
1702.05931 | Francesco Ciompi | Francesco Ciompi, Oscar Geessink, Babak Ehteshami Bejnordi, Gabriel
Silva de Souza, Alexi Baidoshvili, Geert Litjens, Bram van Ginneken, Iris
Nagtegaal, Jeroen van der Laak | The importance of stain normalization in colorectal tissue
classification with convolutional networks | Published in Proceedings of IEEE International Symposium on
Biomedical Imaging (ISBI) 2017 | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The development of reliable imaging biomarkers for the analysis of colorectal
cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images
requires an accurate and reproducible classification of the main tissue
components in the image. In this paper, we propose a system for CRC tissue
classification based on convolutional networks (ConvNets). We investigate the
importance of stain normalization in tissue classification of CRC tissue
samples in H&E-stained images. Furthermore, we report the performance of
ConvNets on a cohort of rectal cancer samples and on an independent publicly
available dataset of colorectal H&E images.
| [
{
"version": "v1",
"created": "Mon, 20 Feb 2017 11:11:50 GMT"
},
{
"version": "v2",
"created": "Tue, 23 May 2017 12:34:17 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Ciompi",
"Francesco",
""
],
[
"Geessink",
"Oscar",
""
],
[
"Bejnordi",
"Babak Ehteshami",
""
],
[
"de Souza",
"Gabriel Silva",
""
],
[
"Baidoshvili",
"Alexi",
""
],
[
"Litjens",
"Geert",
""
],
[
"van Ginneken",
"Bram",
""
],
[
"Nagtegaal",
"Iris",
""
],
[
"van der Laak",
"Jeroen",
""
]
] | TITLE: The importance of stain normalization in colorectal tissue
classification with convolutional networks
ABSTRACT: The development of reliable imaging biomarkers for the analysis of colorectal
cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images
requires an accurate and reproducible classification of the main tissue
components in the image. In this paper, we propose a system for CRC tissue
classification based on convolutional networks (ConvNets). We investigate the
importance of stain normalization in tissue classification of CRC tissue
samples in H&E-stained images. Furthermore, we report the performance of
ConvNets on a cohort of rectal cancer samples and on an independent publicly
available dataset of colorectal H&E images.
| no_new_dataset | 0.952086 |
1704.00390 | Alex Kendall | Alex Kendall and Roberto Cipolla | Geometric Loss Functions for Camera Pose Regression with Deep Learning | CVPR 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep learning has shown to be effective for robust and real-time monocular
image relocalisation. In particular, PoseNet is a deep convolutional neural
network which learns to regress the 6-DOF camera pose from a single image. It
learns to localize using high level features and is robust to difficult
lighting, motion blur and unknown camera intrinsics, where point based SIFT
registration fails. However, it was trained using a naive loss function, with
hyper-parameters which require expensive tuning. In this paper, we give the
problem a more fundamental theoretical treatment. We explore a number of novel
loss functions for learning camera pose which are based on geometry and scene
reprojection error. Additionally we show how to automatically learn an optimal
weighting to simultaneously regress position and orientation. By leveraging
geometry, we demonstrate that our technique significantly improves PoseNet's
performance across datasets ranging from indoor rooms to a small city.
| [
{
"version": "v1",
"created": "Sun, 2 Apr 2017 23:58:22 GMT"
},
{
"version": "v2",
"created": "Tue, 23 May 2017 13:45:48 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Kendall",
"Alex",
""
],
[
"Cipolla",
"Roberto",
""
]
] | TITLE: Geometric Loss Functions for Camera Pose Regression with Deep Learning
ABSTRACT: Deep learning has shown to be effective for robust and real-time monocular
image relocalisation. In particular, PoseNet is a deep convolutional neural
network which learns to regress the 6-DOF camera pose from a single image. It
learns to localize using high level features and is robust to difficult
lighting, motion blur and unknown camera intrinsics, where point based SIFT
registration fails. However, it was trained using a naive loss function, with
hyper-parameters which require expensive tuning. In this paper, we give the
problem a more fundamental theoretical treatment. We explore a number of novel
loss functions for learning camera pose which are based on geometry and scene
reprojection error. Additionally we show how to automatically learn an optimal
weighting to simultaneously regress position and orientation. By leveraging
geometry, we demonstrate that our technique significantly improves PoseNet's
performance across datasets ranging from indoor rooms to a small city.
| no_new_dataset | 0.9455 |
1705.07062 | Nathanael Lemessa Baisa | Nathanael L. Baisa, St\'ephanie Bricq, Alain Lalande | MRI-PET Registration with Automated Algorithm in Pre-clinical Studies | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET)
automatic 3-D registration is implemented and validated for small animal image
volumes so that the high-resolution anatomical MRI information can be fused
with the low spatial resolution of functional PET information for the
localization of lesion that is currently in high demand in the study of tumor
of cancer (oncology) and its corresponding preparation of pharmaceutical drugs.
Though many registration algorithms are developed and applied on human brain
volumes, these methods may not be as efficient on small animal datasets due to
lack of intensity information and often the high anisotropy in voxel
dimensions. Therefore, a fully automatic registration algorithm which can
register not only assumably rigid small animal volumes such as brain but also
deformable organs such as kidney, cardiac and chest is developed using a
combination of global affine and local B-spline transformation models in which
mutual information is used as a similarity criterion. The global affine
registration uses a multi-resolution pyramid on image volumes of 3 levels
whereas in local B-spline registration, a multi-resolution scheme is applied on
the B-spline grid of 2 levels on the finest resolution of the image volumes in
which only the transform itself is affected rather than the image volumes.
Since mutual information lacks sufficient spatial information, PCA is used to
inject it by estimating initial translation and rotation parameters. It is
computationally efficient since it is implemented using C++ and ITK library,
and is qualitatively and quantitatively shown that this PCA-initialized global
registration followed by local registration is in close agreement with expert
manual registration and outperforms the one without PCA initialization tested
on small animal brain and kidney.
| [
{
"version": "v1",
"created": "Fri, 19 May 2017 15:46:30 GMT"
},
{
"version": "v2",
"created": "Tue, 23 May 2017 13:42:39 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Baisa",
"Nathanael L.",
""
],
[
"Bricq",
"Stéphanie",
""
],
[
"Lalande",
"Alain",
""
]
] | TITLE: MRI-PET Registration with Automated Algorithm in Pre-clinical Studies
ABSTRACT: Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET)
automatic 3-D registration is implemented and validated for small animal image
volumes so that the high-resolution anatomical MRI information can be fused
with the low spatial resolution of functional PET information for the
localization of lesion that is currently in high demand in the study of tumor
of cancer (oncology) and its corresponding preparation of pharmaceutical drugs.
Though many registration algorithms are developed and applied on human brain
volumes, these methods may not be as efficient on small animal datasets due to
lack of intensity information and often the high anisotropy in voxel
dimensions. Therefore, a fully automatic registration algorithm which can
register not only assumably rigid small animal volumes such as brain but also
deformable organs such as kidney, cardiac and chest is developed using a
combination of global affine and local B-spline transformation models in which
mutual information is used as a similarity criterion. The global affine
registration uses a multi-resolution pyramid on image volumes of 3 levels
whereas in local B-spline registration, a multi-resolution scheme is applied on
the B-spline grid of 2 levels on the finest resolution of the image volumes in
which only the transform itself is affected rather than the image volumes.
Since mutual information lacks sufficient spatial information, PCA is used to
inject it by estimating initial translation and rotation parameters. It is
computationally efficient since it is implemented using C++ and ITK library,
and is qualitatively and quantitatively shown that this PCA-initialized global
registration followed by local registration is in close agreement with expert
manual registration and outperforms the one without PCA initialization tested
on small animal brain and kidney.
| no_new_dataset | 0.950273 |
1705.08030 | Saeed Maleki | Saeed Maleki, Madanlal Musuvathi, Todd Mytkowicz | Parallel Stochastic Gradient Descent with Sound Combiners | 16 pages, 4 figures | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stochastic gradient descent (SGD) is a well known method for regression and
classification tasks. However, it is an inherently sequential algorithm at each
step, the processing of the current example depends on the parameters learned
from the previous examples. Prior approaches to parallelizing linear learners
using SGD, such as HOGWILD! and ALLREDUCE, do not honor these dependencies
across threads and thus can potentially suffer poor convergence rates and/or
poor scalability. This paper proposes SYMSGD, a parallel SGD algorithm that, to
a first-order approximation, retains the sequential semantics of SGD. Each
thread learns a local model in addition to a model combiner, which allows local
models to be combined to produce the same result as what a sequential SGD would
have produced. This paper evaluates SYMSGD's accuracy and performance on 6
datasets on a shared-memory machine shows upto 11x speedup over our heavily
optimized sequential baseline on 16 cores and 2.2x, on average, faster than
HOGWILD!.
| [
{
"version": "v1",
"created": "Mon, 22 May 2017 22:32:28 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Maleki",
"Saeed",
""
],
[
"Musuvathi",
"Madanlal",
""
],
[
"Mytkowicz",
"Todd",
""
]
] | TITLE: Parallel Stochastic Gradient Descent with Sound Combiners
ABSTRACT: Stochastic gradient descent (SGD) is a well known method for regression and
classification tasks. However, it is an inherently sequential algorithm at each
step, the processing of the current example depends on the parameters learned
from the previous examples. Prior approaches to parallelizing linear learners
using SGD, such as HOGWILD! and ALLREDUCE, do not honor these dependencies
across threads and thus can potentially suffer poor convergence rates and/or
poor scalability. This paper proposes SYMSGD, a parallel SGD algorithm that, to
a first-order approximation, retains the sequential semantics of SGD. Each
thread learns a local model in addition to a model combiner, which allows local
models to be combined to produce the same result as what a sequential SGD would
have produced. This paper evaluates SYMSGD's accuracy and performance on 6
datasets on a shared-memory machine shows upto 11x speedup over our heavily
optimized sequential baseline on 16 cores and 2.2x, on average, faster than
HOGWILD!.
| no_new_dataset | 0.940024 |
1705.08066 | Bo Jiang | Bo Jiang and Chris Ding and Bin Luo | Multiple Images Recovery Using a Single Affine Transformation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many real-world applications, image data often come with noises,
corruptions or large errors. One approach to deal with noise image data is to
use data recovery techniques which aim to recover the true uncorrupted signals
from the observed noise images. In this paper, we first introduce a novel
corruption recovery transformation (CRT) model which aims to recover multiple
(or a collection of) corrupted images using a single affine transformation.
Then, we show that the introduced CRT can be efficiently constructed through
learning from training data. Once CRT is learned, we can recover the true
signals from the new incoming/test corrupted images explicitly. As an
application, we apply our CRT to image recognition task. Experimental results
on six image datasets demonstrate that the proposed CRT model is effective in
recovering noise image data and thus leads to better recognition results.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 03:14:50 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Jiang",
"Bo",
""
],
[
"Ding",
"Chris",
""
],
[
"Luo",
"Bin",
""
]
] | TITLE: Multiple Images Recovery Using a Single Affine Transformation
ABSTRACT: In many real-world applications, image data often come with noises,
corruptions or large errors. One approach to deal with noise image data is to
use data recovery techniques which aim to recover the true uncorrupted signals
from the observed noise images. In this paper, we first introduce a novel
corruption recovery transformation (CRT) model which aims to recover multiple
(or a collection of) corrupted images using a single affine transformation.
Then, we show that the introduced CRT can be efficiently constructed through
learning from training data. Once CRT is learned, we can recover the true
signals from the new incoming/test corrupted images explicitly. As an
application, we apply our CRT to image recognition task. Experimental results
on six image datasets demonstrate that the proposed CRT model is effective in
recovering noise image data and thus leads to better recognition results.
| no_new_dataset | 0.955486 |
1705.08180 | Pietro Morerio | Pietro Morerio and Vittorio Murino | Correlation Alignment by Riemannian Metric for Domain Adaptation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Domain adaptation techniques address the problem of reducing the sensitivity
of machine learning methods to the so-called domain shift, namely the
difference between source (training) and target (test) data distributions. In
particular, unsupervised domain adaptation assumes no labels are available in
the target domain. To this end, aligning second order statistics (covariances)
of target and source domains have proven to be an effective approach ti fill
the gap between the domains. However, covariance matrices do not form a
subspace of the Euclidean space, but live in a Riemannian manifold with
non-positive curvature, making the usual Euclidean metric suboptimal to measure
distances. In this paper, we extend the idea of training a neural network with
a constraint on the covariances of the hidden layer features, by rigorously
accounting for the curved structure of the manifold of symmetric positive
definite matrices. The resulting loss function exploits a theoretically sound
geodesic distance on such manifold. Results show indeed the suboptimal nature
of the Euclidean distance. This makes us able to perform better than previous
approaches on the standard Office dataset, a benchmark for domain adaptation
techniques.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 11:08:48 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Morerio",
"Pietro",
""
],
[
"Murino",
"Vittorio",
""
]
] | TITLE: Correlation Alignment by Riemannian Metric for Domain Adaptation
ABSTRACT: Domain adaptation techniques address the problem of reducing the sensitivity
of machine learning methods to the so-called domain shift, namely the
difference between source (training) and target (test) data distributions. In
particular, unsupervised domain adaptation assumes no labels are available in
the target domain. To this end, aligning second order statistics (covariances)
of target and source domains have proven to be an effective approach ti fill
the gap between the domains. However, covariance matrices do not form a
subspace of the Euclidean space, but live in a Riemannian manifold with
non-positive curvature, making the usual Euclidean metric suboptimal to measure
distances. In this paper, we extend the idea of training a neural network with
a constraint on the covariances of the hidden layer features, by rigorously
accounting for the curved structure of the manifold of symmetric positive
definite matrices. The resulting loss function exploits a theoretically sound
geodesic distance on such manifold. Results show indeed the suboptimal nature
of the Euclidean distance. This makes us able to perform better than previous
approaches on the standard Office dataset, a benchmark for domain adaptation
techniques.
| no_new_dataset | 0.946941 |
1705.08207 | Tam Nguyen | Tam V. Nguyen, Luoqi Liu | Salient Object Detection with Semantic Priors | accepted to IJCAI 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Salient object detection has increasingly become a popular topic in cognitive
and computational sciences, including computer vision and artificial
intelligence research. In this paper, we propose integrating \textit{semantic
priors} into the salient object detection process. Our algorithm consists of
three basic steps. Firstly, the explicit saliency map is obtained based on the
semantic segmentation refined by the explicit saliency priors learned from the
data. Next, the implicit saliency map is computed based on a trained model
which maps the implicit saliency priors embedded into regional features with
the saliency values. Finally, the explicit semantic map and the implicit map
are adaptively fused to form a pixel-accurate saliency map which uniformly
covers the objects of interest. We further evaluate the proposed framework on
two challenging datasets, namely, ECSSD and HKUIS. The extensive experimental
results demonstrate that our method outperforms other state-of-the-art methods.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 12:24:09 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Nguyen",
"Tam V.",
""
],
[
"Liu",
"Luoqi",
""
]
] | TITLE: Salient Object Detection with Semantic Priors
ABSTRACT: Salient object detection has increasingly become a popular topic in cognitive
and computational sciences, including computer vision and artificial
intelligence research. In this paper, we propose integrating \textit{semantic
priors} into the salient object detection process. Our algorithm consists of
three basic steps. Firstly, the explicit saliency map is obtained based on the
semantic segmentation refined by the explicit saliency priors learned from the
data. Next, the implicit saliency map is computed based on a trained model
which maps the implicit saliency priors embedded into regional features with
the saliency values. Finally, the explicit semantic map and the implicit map
are adaptively fused to form a pixel-accurate saliency map which uniformly
covers the objects of interest. We further evaluate the proposed framework on
two challenging datasets, namely, ECSSD and HKUIS. The extensive experimental
results demonstrate that our method outperforms other state-of-the-art methods.
| no_new_dataset | 0.951414 |
1705.08214 | Michael Gygli | Michael Gygli | Ridiculously Fast Shot Boundary Detection with Fully Convolutional
Neural Networks | null | null | null | null | cs.CV cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Shot boundary detection (SBD) is an important component of many video
analysis tasks, such as action recognition, video indexing, summarization and
editing. Previous work typically used a combination of low-level features like
color histograms, in conjunction with simple models such as SVMs. Instead, we
propose to learn shot detection end-to-end, from pixels to final shot
boundaries. For training such a model, we rely on our insight that all shot
boundaries are generated. Thus, we create a dataset with one million frames and
automatically generated transitions such as cuts, dissolves and fades. In order
to efficiently analyze hours of videos, we propose a Convolutional Neural
Network (CNN) which is fully convolutional in time, thus allowing to use a
large temporal context without the need to repeatedly processing frames. With
this architecture our method obtains state-of-the-art results while running at
an unprecedented speed of more than 120x real-time.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 12:39:51 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Gygli",
"Michael",
""
]
] | TITLE: Ridiculously Fast Shot Boundary Detection with Fully Convolutional
Neural Networks
ABSTRACT: Shot boundary detection (SBD) is an important component of many video
analysis tasks, such as action recognition, video indexing, summarization and
editing. Previous work typically used a combination of low-level features like
color histograms, in conjunction with simple models such as SVMs. Instead, we
propose to learn shot detection end-to-end, from pixels to final shot
boundaries. For training such a model, we rely on our insight that all shot
boundaries are generated. Thus, we create a dataset with one million frames and
automatically generated transitions such as cuts, dissolves and fades. In order
to efficiently analyze hours of videos, we propose a Convolutional Neural
Network (CNN) which is fully convolutional in time, thus allowing to use a
large temporal context without the need to repeatedly processing frames. With
this architecture our method obtains state-of-the-art results while running at
an unprecedented speed of more than 120x real-time.
| new_dataset | 0.950732 |
1705.08260 | Menglong Ye | Menglong Ye and Edward Johns and Ankur Handa and Lin Zhang and Philip
Pratt and Guang-Zhong Yang | Self-Supervised Siamese Learning on Stereo Image Pairs for Depth
Estimation in Robotic Surgery | A two-page short report to be presented at the Hamlyn Symposium on
Medical Robotics 2017. An extension of this work is on progress | null | null | null | cs.CV cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robotic surgery has become a powerful tool for performing minimally invasive
procedures, providing advantages in dexterity, precision, and 3D vision, over
traditional surgery. One popular robotic system is the da Vinci surgical
platform, which allows preoperative information to be incorporated into live
procedures using Augmented Reality (AR). Scene depth estimation is a
prerequisite for AR, as accurate registration requires 3D correspondences
between preoperative and intraoperative organ models. In the past decade, there
has been much progress on depth estimation for surgical scenes, such as using
monocular or binocular laparoscopes [1,2]. More recently, advances in deep
learning have enabled depth estimation via Convolutional Neural Networks (CNNs)
[3], but training requires a large image dataset with ground truth depths.
Inspired by [4], we propose a deep learning framework for surgical scene depth
estimation using self-supervision for scalable data acquisition. Our framework
consists of an autoencoder for depth prediction, and a differentiable spatial
transformer for training the autoencoder on stereo image pairs without ground
truth depths. Validation was conducted on stereo videos collected in robotic
partial nephrectomy.
| [
{
"version": "v1",
"created": "Wed, 17 May 2017 11:10:49 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Ye",
"Menglong",
""
],
[
"Johns",
"Edward",
""
],
[
"Handa",
"Ankur",
""
],
[
"Zhang",
"Lin",
""
],
[
"Pratt",
"Philip",
""
],
[
"Yang",
"Guang-Zhong",
""
]
] | TITLE: Self-Supervised Siamese Learning on Stereo Image Pairs for Depth
Estimation in Robotic Surgery
ABSTRACT: Robotic surgery has become a powerful tool for performing minimally invasive
procedures, providing advantages in dexterity, precision, and 3D vision, over
traditional surgery. One popular robotic system is the da Vinci surgical
platform, which allows preoperative information to be incorporated into live
procedures using Augmented Reality (AR). Scene depth estimation is a
prerequisite for AR, as accurate registration requires 3D correspondences
between preoperative and intraoperative organ models. In the past decade, there
has been much progress on depth estimation for surgical scenes, such as using
monocular or binocular laparoscopes [1,2]. More recently, advances in deep
learning have enabled depth estimation via Convolutional Neural Networks (CNNs)
[3], but training requires a large image dataset with ground truth depths.
Inspired by [4], we propose a deep learning framework for surgical scene depth
estimation using self-supervision for scalable data acquisition. Our framework
consists of an autoencoder for depth prediction, and a differentiable spatial
transformer for training the autoencoder on stereo image pairs without ground
truth depths. Validation was conducted on stereo videos collected in robotic
partial nephrectomy.
| no_new_dataset | 0.947381 |
1705.08374 | Carlos Becker | Carlos Becker, Nicolai H\"ani, Elena Rosinskaya, Emmanuel d'Angelo,
Christoph Strecha | Classification of Aerial Photogrammetric 3D Point Clouds | ISPRS 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a powerful method to extract per-point semantic class labels from
aerialphotogrammetry data. Labeling this kind of data is important for tasks
such as environmental modelling, object classification and scene understanding.
Unlike previous point cloud classification methods that rely exclusively on
geometric features, we show that incorporating color information yields a
significant increase in accuracy in detecting semantic classes. We test our
classification method on three real-world photogrammetry datasets that were
generated with Pix4Dmapper Pro, and with varying point densities. We show that
off-the-shelf machine learning techniques coupled with our new features allow
us to train highly accurate classifiers that generalize well to unseen data,
processing point clouds containing 10 million points in less than 3 minutes on
a desktop computer.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 15:44:40 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Becker",
"Carlos",
""
],
[
"Häni",
"Nicolai",
""
],
[
"Rosinskaya",
"Elena",
""
],
[
"d'Angelo",
"Emmanuel",
""
],
[
"Strecha",
"Christoph",
""
]
] | TITLE: Classification of Aerial Photogrammetric 3D Point Clouds
ABSTRACT: We present a powerful method to extract per-point semantic class labels from
aerialphotogrammetry data. Labeling this kind of data is important for tasks
such as environmental modelling, object classification and scene understanding.
Unlike previous point cloud classification methods that rely exclusively on
geometric features, we show that incorporating color information yields a
significant increase in accuracy in detecting semantic classes. We test our
classification method on three real-world photogrammetry datasets that were
generated with Pix4Dmapper Pro, and with varying point densities. We show that
off-the-shelf machine learning techniques coupled with our new features allow
us to train highly accurate classifiers that generalize well to unseen data,
processing point clouds containing 10 million points in less than 3 minutes on
a desktop computer.
| no_new_dataset | 0.947332 |
1705.08409 | Leye Wang | Leye Wang, Xu Geng, Jintao Ke, Chen Peng, Xiaojuan Ma, Daqing Zhang,
Qiang Yang | Ridesourcing Car Detection by Transfer Learning | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ridesourcing platforms like Uber and Didi are getting more and more popular
around the world. However, unauthorized ridesourcing activities taking
advantages of the sharing economy can greatly impair the healthy development of
this emerging industry. As the first step to regulate on-demand ride services
and eliminate black market, we design a method to detect ridesourcing cars from
a pool of cars based on their trajectories. Since licensed ridesourcing car
traces are not openly available and may be completely missing in some cities
due to legal issues, we turn to transferring knowledge from public transport
open data, i.e, taxis and buses, to ridesourcing detection among ordinary
vehicles. We propose a two-stage transfer learning framework. In Stage 1, we
take taxi and bus data as input to learn a random forest (RF) classifier using
trajectory features shared by taxis/buses and ridesourcing/other cars. Then, we
use the RF to label all the candidate cars. In Stage 2, leveraging the subset
of high confident labels from the previous stage as input, we further learn a
convolutional neural network (CNN) classifier for ridesourcing detection, and
iteratively refine RF and CNN, as well as the feature set, via a co-training
process. Finally, we use the resulting ensemble of RF and CNN to identify the
ridesourcing cars in the candidate pool. Experiments on real car, taxi and bus
traces show that our transfer learning framework, with no need of a pre-labeled
ridesourcing dataset, can achieve similar accuracy as the supervised learning
methods.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 16:59:29 GMT"
}
] | 2017-05-24T00:00:00 | [
[
"Wang",
"Leye",
""
],
[
"Geng",
"Xu",
""
],
[
"Ke",
"Jintao",
""
],
[
"Peng",
"Chen",
""
],
[
"Ma",
"Xiaojuan",
""
],
[
"Zhang",
"Daqing",
""
],
[
"Yang",
"Qiang",
""
]
] | TITLE: Ridesourcing Car Detection by Transfer Learning
ABSTRACT: Ridesourcing platforms like Uber and Didi are getting more and more popular
around the world. However, unauthorized ridesourcing activities taking
advantages of the sharing economy can greatly impair the healthy development of
this emerging industry. As the first step to regulate on-demand ride services
and eliminate black market, we design a method to detect ridesourcing cars from
a pool of cars based on their trajectories. Since licensed ridesourcing car
traces are not openly available and may be completely missing in some cities
due to legal issues, we turn to transferring knowledge from public transport
open data, i.e, taxis and buses, to ridesourcing detection among ordinary
vehicles. We propose a two-stage transfer learning framework. In Stage 1, we
take taxi and bus data as input to learn a random forest (RF) classifier using
trajectory features shared by taxis/buses and ridesourcing/other cars. Then, we
use the RF to label all the candidate cars. In Stage 2, leveraging the subset
of high confident labels from the previous stage as input, we further learn a
convolutional neural network (CNN) classifier for ridesourcing detection, and
iteratively refine RF and CNN, as well as the feature set, via a co-training
process. Finally, we use the resulting ensemble of RF and CNN to identify the
ridesourcing cars in the candidate pool. Experiments on real car, taxi and bus
traces show that our transfer learning framework, with no need of a pre-labeled
ridesourcing dataset, can achieve similar accuracy as the supervised learning
methods.
| no_new_dataset | 0.944893 |
1506.06112 | Ethan Rudd | Ethan M. Rudd, Lalit P. Jain, Walter J. Scheirer, Terrance E. Boult | The Extreme Value Machine | Pre-print of a manuscript accepted to the IEEE Transactions on
Pattern Analysis and Machine Intelligence (T-PAMI) journal | null | 10.1109/TPAMI.2017.2707495 | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is often desirable to be able to recognize when inputs to a recognition
function learned in a supervised manner correspond to classes unseen at
training time. With this ability, new class labels could be assigned to these
inputs by a human operator, allowing them to be incorporated into the
recognition function --- ideally under an efficient incremental update
mechanism. While good algorithms that assume inputs from a fixed set of classes
exist, e.g., artificial neural networks and kernel machines, it is not
immediately obvious how to extend them to perform incremental learning in the
presence of unknown query classes. Existing algorithms take little to no
distributional information into account when learning recognition functions and
lack a strong theoretical foundation. We address this gap by formulating a
novel, theoretically sound classifier --- the Extreme Value Machine (EVM). The
EVM has a well-grounded interpretation derived from statistical Extreme Value
Theory (EVT), and is the first classifier to be able to perform nonlinear
kernel-free variable bandwidth incremental learning. Compared to other
classifiers in the same deep network derived feature space, the EVM is accurate
and efficient on an established benchmark partition of the ImageNet dataset.
| [
{
"version": "v1",
"created": "Fri, 19 Jun 2015 19:04:54 GMT"
},
{
"version": "v2",
"created": "Tue, 12 Jan 2016 00:21:24 GMT"
},
{
"version": "v3",
"created": "Wed, 18 May 2016 00:57:06 GMT"
},
{
"version": "v4",
"created": "Sun, 21 May 2017 01:47:04 GMT"
}
] | 2017-05-23T00:00:00 | [
[
"Rudd",
"Ethan M.",
""
],
[
"Jain",
"Lalit P.",
""
],
[
"Scheirer",
"Walter J.",
""
],
[
"Boult",
"Terrance E.",
""
]
] | TITLE: The Extreme Value Machine
ABSTRACT: It is often desirable to be able to recognize when inputs to a recognition
function learned in a supervised manner correspond to classes unseen at
training time. With this ability, new class labels could be assigned to these
inputs by a human operator, allowing them to be incorporated into the
recognition function --- ideally under an efficient incremental update
mechanism. While good algorithms that assume inputs from a fixed set of classes
exist, e.g., artificial neural networks and kernel machines, it is not
immediately obvious how to extend them to perform incremental learning in the
presence of unknown query classes. Existing algorithms take little to no
distributional information into account when learning recognition functions and
lack a strong theoretical foundation. We address this gap by formulating a
novel, theoretically sound classifier --- the Extreme Value Machine (EVM). The
EVM has a well-grounded interpretation derived from statistical Extreme Value
Theory (EVT), and is the first classifier to be able to perform nonlinear
kernel-free variable bandwidth incremental learning. Compared to other
classifiers in the same deep network derived feature space, the EVM is accurate
and efficient on an established benchmark partition of the ImageNet dataset.
| no_new_dataset | 0.943504 |
1507.06951 | David Weyburne | David Weyburne | A Cautionary Note on the Zagarola and Smits Similarity Parameter for the
Turbulent Boundary Layer | 18 pages, 11 figures, 1 appendix, Latest version offers improved
readability | null | null | null | physics.flu-dyn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Zagarola and Smits developed an empirical velocity parameter for scaling the
outer region of the turbulent boundary layer velocity profile that has been
widely applied to experimental datasets. Plots of the scaled defect profiles
indicate that most datasets display similar-like behavior using the Zagarola
and Smits scaling parameter. In the work herein, it is shown that the common
practice of finding similarity behavior using the defect profile is often
incomplete in the sense that not all of the criteria for similarity have been
checked for compliance. When full compliance is checked, it is found that most
of the datasets which display defect similarity do not satisfy all the criteria
required for similarity. The nature of this contradiction and noncompliance is
described in detail. It is shown that the original datasets used by Zagarola
and Smits display this flawed similarity behavior. Hence, a careful
reassessment of any claims in the literature is required for those groups that
attempted to use the defect profile and the Zagarola and Smits type of velocity
scaling parameter to assert similarity of the velocity profile.
| [
{
"version": "v1",
"created": "Thu, 23 Jul 2015 19:57:45 GMT"
},
{
"version": "v2",
"created": "Mon, 31 Aug 2015 16:51:38 GMT"
},
{
"version": "v3",
"created": "Tue, 15 Mar 2016 18:06:34 GMT"
},
{
"version": "v4",
"created": "Sat, 20 May 2017 16:15:58 GMT"
}
] | 2017-05-23T00:00:00 | [
[
"Weyburne",
"David",
""
]
] | TITLE: A Cautionary Note on the Zagarola and Smits Similarity Parameter for the
Turbulent Boundary Layer
ABSTRACT: Zagarola and Smits developed an empirical velocity parameter for scaling the
outer region of the turbulent boundary layer velocity profile that has been
widely applied to experimental datasets. Plots of the scaled defect profiles
indicate that most datasets display similar-like behavior using the Zagarola
and Smits scaling parameter. In the work herein, it is shown that the common
practice of finding similarity behavior using the defect profile is often
incomplete in the sense that not all of the criteria for similarity have been
checked for compliance. When full compliance is checked, it is found that most
of the datasets which display defect similarity do not satisfy all the criteria
required for similarity. The nature of this contradiction and noncompliance is
described in detail. It is shown that the original datasets used by Zagarola
and Smits display this flawed similarity behavior. Hence, a careful
reassessment of any claims in the literature is required for those groups that
attempted to use the defect profile and the Zagarola and Smits type of velocity
scaling parameter to assert similarity of the velocity profile.
| no_new_dataset | 0.954095 |
1511.04511 | Ziming Zhang | Ziming Zhang, Yun Liu, Xi Chen, Yanjun Zhu, Ming-Ming Cheng, Venkatesh
Saligrama, and Philip H.S. Torr | Sequential Optimization for Efficient High-Quality Object Proposal
Generation | Accepted by TPAMI | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We are motivated by the need for a generic object proposal generation
algorithm which achieves good balance between object detection recall, proposal
localization quality and computational efficiency. We propose a novel object
proposal algorithm, BING++, which inherits the virtue of good computational
efficiency of BING but significantly improves its proposal localization
quality. At high level we formulate the problem of object proposal generation
from a novel probabilistic perspective, based on which our BING++ manages to
improve the localization quality by employing edges and segments to estimate
object boundaries and update the proposals sequentially. We propose learning
the parameters efficiently by searching for approximate solutions in a
quantized parameter space for complexity reduction. We demonstrate the
generalization of BING++ with the same fixed parameters across different object
classes and datasets. Empirically our BING++ can run at half speed of BING on
CPU, but significantly improve the localization quality by 18.5% and 16.7% on
both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other
state-of-the-art approaches, BING++ can achieve comparable performance, but run
significantly faster.
| [
{
"version": "v1",
"created": "Sat, 14 Nov 2015 05:45:47 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Jul 2016 04:35:15 GMT"
},
{
"version": "v3",
"created": "Mon, 22 May 2017 17:23:07 GMT"
}
] | 2017-05-23T00:00:00 | [
[
"Zhang",
"Ziming",
""
],
[
"Liu",
"Yun",
""
],
[
"Chen",
"Xi",
""
],
[
"Zhu",
"Yanjun",
""
],
[
"Cheng",
"Ming-Ming",
""
],
[
"Saligrama",
"Venkatesh",
""
],
[
"Torr",
"Philip H. S.",
""
]
] | TITLE: Sequential Optimization for Efficient High-Quality Object Proposal
Generation
ABSTRACT: We are motivated by the need for a generic object proposal generation
algorithm which achieves good balance between object detection recall, proposal
localization quality and computational efficiency. We propose a novel object
proposal algorithm, BING++, which inherits the virtue of good computational
efficiency of BING but significantly improves its proposal localization
quality. At high level we formulate the problem of object proposal generation
from a novel probabilistic perspective, based on which our BING++ manages to
improve the localization quality by employing edges and segments to estimate
object boundaries and update the proposals sequentially. We propose learning
the parameters efficiently by searching for approximate solutions in a
quantized parameter space for complexity reduction. We demonstrate the
generalization of BING++ with the same fixed parameters across different object
classes and datasets. Empirically our BING++ can run at half speed of BING on
CPU, but significantly improve the localization quality by 18.5% and 16.7% on
both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other
state-of-the-art approaches, BING++ can achieve comparable performance, but run
significantly faster.
| no_new_dataset | 0.948537 |
1611.01484 | Ankan Bansal | Ankan Bansal, Anirudh Nanduri, Carlos Castillo, Rajeev Ranjan, Rama
Chellappa | UMDFaces: An Annotated Face Dataset for Training Deep Networks | Updates: Verified keypoints, removed duplicate subjects, released
test protocol | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent progress in face detection (including keypoint detection), and
recognition is mainly being driven by (i) deeper convolutional neural network
architectures, and (ii) larger datasets. However, most of the large datasets
are maintained by private companies and are not publicly available. The
academic computer vision community needs larger and more varied datasets to
make further progress.
In this paper we introduce a new face dataset, called UMDFaces, which has
367,888 annotated faces of 8,277 subjects. We also introduce a new face
recognition evaluation protocol which will help advance the state-of-the-art in
this area. We discuss how a large dataset can be collected and annotated using
human annotators and deep networks. We provide human curated bounding boxes for
faces. We also provide estimated pose (roll, pitch and yaw), locations of
twenty-one key-points and gender information generated by a pre-trained neural
network. In addition, the quality of keypoint annotations has been verified by
humans for about 115,000 images. Finally, we compare the quality of the dataset
with other publicly available face datasets at similar scales.
| [
{
"version": "v1",
"created": "Fri, 4 Nov 2016 18:37:41 GMT"
},
{
"version": "v2",
"created": "Sun, 21 May 2017 08:00:42 GMT"
}
] | 2017-05-23T00:00:00 | [
[
"Bansal",
"Ankan",
""
],
[
"Nanduri",
"Anirudh",
""
],
[
"Castillo",
"Carlos",
""
],
[
"Ranjan",
"Rajeev",
""
],
[
"Chellappa",
"Rama",
""
]
] | TITLE: UMDFaces: An Annotated Face Dataset for Training Deep Networks
ABSTRACT: Recent progress in face detection (including keypoint detection), and
recognition is mainly being driven by (i) deeper convolutional neural network
architectures, and (ii) larger datasets. However, most of the large datasets
are maintained by private companies and are not publicly available. The
academic computer vision community needs larger and more varied datasets to
make further progress.
In this paper we introduce a new face dataset, called UMDFaces, which has
367,888 annotated faces of 8,277 subjects. We also introduce a new face
recognition evaluation protocol which will help advance the state-of-the-art in
this area. We discuss how a large dataset can be collected and annotated using
human annotators and deep networks. We provide human curated bounding boxes for
faces. We also provide estimated pose (roll, pitch and yaw), locations of
twenty-one key-points and gender information generated by a pre-trained neural
network. In addition, the quality of keypoint annotations has been verified by
humans for about 115,000 images. Finally, we compare the quality of the dataset
with other publicly available face datasets at similar scales.
| new_dataset | 0.962462 |
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