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2305.10446 | 2023-05-15T12:06:31Z | Emotion Recognition based on Psychological Components in Guided
Narratives for Emotion Regulation | [
"Gustave Cortal",
"Alain Finkel",
"Patrick Paroubek",
"Lina Ye"
] | Emotion regulation is a crucial element in dealing with emotional events and
has positive effects on mental health. This paper aims to provide a more
comprehensive understanding of emotional events by introducing a new French
corpus of emotional narratives collected using a questionnaire for emotion
regulation. We follow the theoretical framework of the Component Process Model
which considers emotions as dynamic processes composed of four interrelated
components (behavior, feeling, thinking and territory). Each narrative is
related to a discrete emotion and is structured based on all emotion components
by the writers. We study the interaction of components and their impact on
emotion classification with machine learning methods and pre-trained language
models. Our results show that each component improves prediction performance,
and that the best results are achieved by jointly considering all components.
Our results also show the effectiveness of pre-trained language models in
predicting discrete emotion from certain components, which reveal differences
in how emotion components are expressed. | [
"cs.CL",
"cs.AI"
] | false |
2305.10447 | 2023-05-15T16:39:35Z | The Effectiveness of a Dynamic Loss Function in Neural Network Based
Automated Essay Scoring | [
"Oscar Morris"
] | Neural networks and in particular the attention mechanism have brought
significant advances to the field of Automated Essay Scoring. Many of these
systems use a regression-based model which may be prone to underfitting when
the model only predicts the mean of the training data. In this paper, we
present a dynamic loss function that creates an incentive for the model to
predict with the correct distribution, as well as predicting the correct
values. Our loss function achieves this goal without sacrificing any
performance achieving a Quadratic Weighted Kappa score of 0.752 on the
Automated Student Assessment Prize Automated Essay Scoring dataset. | [
"cs.CL",
"cs.AI"
] | false |
2305.16328 | 2023-05-15T03:19:42Z | Semantic Composition in Visually Grounded Language Models | [
"Rohan Pandey"
] | What is sentence meaning and its ideal representation? Much of the expressive
power of human language derives from semantic composition, the mind's ability
to represent meaning hierarchically & relationally over constituents. At the
same time, much sentential meaning is outside the text and requires grounding
in sensory, motor, and experiential modalities to be adequately learned.
Although large language models display considerable compositional ability,
recent work shows that visually-grounded language models drastically fail to
represent compositional structure. In this thesis, we explore whether & how
models compose visually grounded semantics, and how we might improve their
ability to do so.
Specifically, we introduce 1) WinogroundVQA, a new compositional visual
question answering benchmark, 2) Syntactic Neural Module Distillation, a
measure of compositional ability in sentence embedding models, 3) Causal
Tracing for Image Captioning Models to locate neural representations vital for
vision-language composition, 4) Syntactic MeanPool to inject a compositional
inductive bias into sentence embeddings, and 5) Cross-modal Attention
Congruence Regularization, a self-supervised objective function for
vision-language relation alignment. We close by discussing connections of our
work to neuroscience, psycholinguistics, formal semantics, and philosophy. | [
"cs.CL",
"cs.LG"
] | false |
2305.08706 | 2023-05-15T15:09:18Z | Understanding and Bridging the Modality Gap for Speech Translation | [
"Qingkai Fang",
"Yang Feng"
] | How to achieve better end-to-end speech translation (ST) by leveraging (text)
machine translation (MT) data? Among various existing techniques, multi-task
learning is one of the effective ways to share knowledge between ST and MT in
which additional MT data can help to learn source-to-target mapping. However,
due to the differences between speech and text, there is always a gap between
ST and MT. In this paper, we first aim to understand this modality gap from the
target-side representation differences, and link the modality gap to another
well-known problem in neural machine translation: exposure bias. We find that
the modality gap is relatively small during training except for some difficult
cases, but keeps increasing during inference due to the cascading effect. To
address these problems, we propose the Cross-modal Regularization with
Scheduled Sampling (Cress) method. Specifically, we regularize the output
predictions of ST and MT, whose target-side contexts are derived by sampling
between ground truth words and self-generated words with a varying probability.
Furthermore, we introduce token-level adaptive training which assigns different
training weights to target tokens to handle difficult cases with large modality
gaps. Experiments and analysis show that our approach effectively bridges the
modality gap, and achieves promising results in all eight directions of the
MuST-C dataset. | [
"cs.CL",
"cs.SD",
"eess.AS",
"I.2.7"
] | false |
2305.08709 | 2023-05-15T15:12:40Z | Back Translation for Speech-to-text Translation Without Transcripts | [
"Qingkai Fang",
"Yang Feng"
] | The success of end-to-end speech-to-text translation (ST) is often achieved
by utilizing source transcripts, e.g., by pre-training with automatic speech
recognition (ASR) and machine translation (MT) tasks, or by introducing
additional ASR and MT data. Unfortunately, transcripts are only sometimes
available since numerous unwritten languages exist worldwide. In this paper, we
aim to utilize large amounts of target-side monolingual data to enhance ST
without transcripts. Motivated by the remarkable success of back translation in
MT, we develop a back translation algorithm for ST (BT4ST) to synthesize pseudo
ST data from monolingual target data. To ease the challenges posed by
short-to-long generation and one-to-many mapping, we introduce self-supervised
discrete units and achieve back translation by cascading a target-to-unit model
and a unit-to-speech model. With our synthetic ST data, we achieve an average
boost of 2.3 BLEU on MuST-C En-De, En-Fr, and En-Es datasets. More experiments
show that our method is especially effective in low-resource scenarios. | [
"cs.CL",
"cs.SD",
"eess.AS",
"I.2.7"
] | false |
2305.08848 | 2023-05-15T17:59:01Z | Small Models are Valuable Plug-ins for Large Language Models | [
"Canwen Xu",
"Yichong Xu",
"Shuohang Wang",
"Yang Liu",
"Chenguang Zhu",
"Julian McAuley"
] | Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their
weights are often publicly unavailable and their immense sizes make the models
difficult to be tuned with common hardware. As a result, effectively tuning
these models with large-scale supervised data can be challenging. As an
alternative, In-Context Learning (ICL) can only use a small number of
supervised examples due to context length limits. In this paper, we propose
Super In-Context Learning (SuperICL) which allows black-box LLMs to work with
locally fine-tuned smaller models, resulting in superior performance on
supervised tasks. Our experiments demonstrate that SuperICL can improve
performance beyond state-of-the-art fine-tuned models while addressing the
instability problem of in-context learning. Furthermore, SuperICL can enhance
the capabilities of smaller models, such as multilinguality and
interpretability. | [
"cs.CL",
"cs.AI",
"cs.LG"
] | true |
2305.08978 | 2023-05-15T19:40:28Z | An assessment of measuring local levels of homelessness through proxy
social media signals | [
"Yoshi Meke Bird",
"Sarah E. Grobe",
"Michael V. Arnold",
"Sean P. Rogers",
"Mikaela I. Fudolig",
"Julia Witte Zimmerman",
"Christopher M. Danforth",
"Peter Sheridan Dodds"
] | Recent studies suggest social media activity can function as a proxy for
measures of state-level public health, detectable through natural language
processing. We present results of our efforts to apply this approach to
estimate homelessness at the state level throughout the US during the period
2010-2019 and 2022 using a dataset of roughly 1 million geotagged tweets
containing the substring ``homeless.'' Correlations between
homelessness-related tweet counts and ranked per capita homelessness volume,
but not general-population densities, suggest a relationship between the
likelihood of Twitter users to personally encounter or observe homelessness in
their everyday lives and their likelihood to communicate about it online. An
increase to the log-odds of ``homeless'' appearing in an English-language
tweet, as well as an acceleration in the increase in average tweet sentiment,
suggest that tweets about homelessness are also affected by trends at the
nation-scale. Additionally, changes to the lexical content of tweets over time
suggest that reversals to the polarity of national or state-level trends may be
detectable through an increase in political or service-sector language over the
semantics of charity or direct appeals. An analysis of user account type also
revealed changes to Twitter-use patterns by accounts authored by individuals
versus entities that may provide an additional signal to confirm changes to
homelessness density in a given jurisdiction. While a computational approach to
social media analysis may provide a low-cost, real-time dataset rich with
information about nationwide and localized impacts of homelessness and
homelessness policy, we find that practical issues abound, limiting the
potential of social media as a proxy to complement other measures of
homelessness. | [
"cs.SI",
"cs.CL",
"cs.CY"
] | false |
2305.09688 | 2023-05-15T18:00:39Z | OOD-Speech: A Large Bengali Speech Recognition Dataset for
Out-of-Distribution Benchmarking | [
"Fazle Rabbi Rakib",
"Souhardya Saha Dip",
"Samiul Alam",
"Nazia Tasnim",
"Md. Istiak Hossain Shihab",
"Md. Nazmuddoha Ansary",
"Syed Mobassir Hossen",
"Marsia Haque Meghla",
"Mamunur Mamun",
"Farig Sadeque",
"Sayma Sultana Chowdhury",
"Tahsin Reasat",
"Asif Sushmit",
"Ahmed Imtiaz Humayun"
] | We present OOD-Speech, the first out-of-distribution (OOD) benchmarking
dataset for Bengali automatic speech recognition (ASR). Being one of the most
spoken languages globally, Bengali portrays large diversity in dialects and
prosodic features, which demands ASR frameworks to be robust towards
distribution shifts. For example, islamic religious sermons in Bengali are
delivered with a tonality that is significantly different from regular speech.
Our training dataset is collected via massively online crowdsourcing campaigns
which resulted in 1177.94 hours collected and curated from $22,645$ native
Bengali speakers from South Asia. Our test dataset comprises 23.03 hours of
speech collected and manually annotated from 17 different sources, e.g.,
Bengali TV drama, Audiobook, Talk show, Online class, and Islamic sermons to
name a few. OOD-Speech is jointly the largest publicly available speech
dataset, as well as the first out-of-distribution ASR benchmarking dataset for
Bengali. | [
"eess.AS",
"cs.CL",
"cs.LG"
] | false |
2305.08344 | 2023-05-15T04:43:14Z | Enhancing Label Sharing Efficiency in Complementary-Label Learning with
Label Augmentation | [
"Wei-I Lin",
"Gang Niu",
"Hsuan-Tien Lin",
"Masashi Sugiyama"
] | Complementary-label Learning (CLL) is a form of weakly supervised learning
that trains an ordinary classifier using only complementary labels, which are
the classes that certain instances do not belong to. While existing CLL studies
typically use novel loss functions or training techniques to solve this
problem, few studies focus on how complementary labels collectively provide
information to train the ordinary classifier. In this paper, we fill the gap by
analyzing the implicit sharing of complementary labels on nearby instances
during training. Our analysis reveals that the efficiency of implicit label
sharing is closely related to the performance of existing CLL models. Based on
this analysis, we propose a novel technique that enhances the sharing
efficiency via complementary-label augmentation, which explicitly propagates
additional complementary labels to each instance. We carefully design the
augmentation process to enrich the data with new and accurate complementary
labels, which provide CLL models with fresh and valuable information to enhance
the sharing efficiency. We then verify our proposed technique by conducting
thorough experiments on both synthetic and real-world datasets. Our results
confirm that complementary-label augmentation can systematically improve
empirical performance over state-of-the-art CLL models. | [
"cs.LG"
] | false |
2305.08367 | 2023-05-15T06:00:02Z | Fast Submodular Function Maximization | [
"Lianke Qin",
"Zhao Song",
"Yitan Wang"
] | Submodular functions have many real-world applications, such as document
summarization, sensor placement, and image segmentation. For all these
applications, the key building block is how to compute the maximum value of a
submodular function efficiently. We consider both the online and offline
versions of the problem: in each iteration, the data set changes incrementally
or is not changed, and a user can issue a query to maximize the function on a
given subset of the data. The user can be malicious, issuing queries based on
previous query results to break the competitive ratio for the online algorithm.
Today, the best-known algorithm for online submodular function maximization has
a running time of $O(n k d^2)$ where $n$ is the total number of elements, $d$
is the feature dimension and $k$ is the number of elements to be selected. We
propose a new method based on a novel search tree data structure. Our algorithm
only takes $\widetilde{O}(nk + kd^2 + nd)$ time. | [
"cs.LG"
] | false |
2305.08579 | 2023-05-15T12:05:03Z | Fast Inference of Tree Ensembles on ARM Devices | [
"Simon Koschel",
"Sebastian Buschjäger",
"Claudio Lucchese",
"Katharina Morik"
] | With the ongoing integration of Machine Learning models into everyday life,
e.g. in the form of the Internet of Things (IoT), the evaluation of learned
models becomes more and more an important issue. Tree ensembles are one of the
best black-box classifiers available and routinely outperform more complex
classifiers. While the fast application of tree ensembles has already been
studied in the literature for Intel CPUs, they have not yet been studied in the
context of ARM CPUs which are more dominant for IoT applications. In this
paper, we convert the popular QuickScorer algorithm and its siblings from
Intel's AVX to ARM's NEON instruction set. Second, we extend our implementation
from ranking models to classification models such as Random Forests. Third, we
investigate the effects of using fixed-point quantization in Random Forests.
Our study shows that a careful implementation of tree traversal on ARM CPUs
leads to a speed-up of up to 9.4 compared to a reference implementation.
Moreover, quantized models seem to outperform models using floating-point
values in terms of speed in almost all cases, with a neglectable impact on the
predictive performance of the model. Finally, our study highlights
architectural differences between ARM and Intel CPUs and between different ARM
devices that imply that the best implementation depends on both the specific
forest as well as the specific device used for deployment. | [
"cs.LG"
] | false |
2305.08600 | 2023-05-15T12:30:11Z | Evaluating Splitting Approaches in the Context of Student Dropout
Prediction | [
"Bruno de M. Barros",
"Hugo A. D. do Nascimento",
"Raphael Guedes",
"Sandro E. Monsueto"
] | The prediction of academic dropout, with the aim of preventing it, is one of
the current challenges of higher education institutions. Machine learning
techniques are a great ally in this task. However, attention is needed in the
way that academic data are used by such methods, so that it reflects the
reality of the prediction problem under study and allows achieving good
results. In this paper, we study strategies for splitting and using academic
data in order to create training and testing sets. Through a conceptual
analysis and experiments with data from a public higher education institution,
we show that a random proportional data splitting, and even a simple temporal
splitting are not suitable for dropout prediction. The study indicates that a
temporal splitting combined with a time-based selection of the students'
incremental academic histories leads to the best strategy for the problem in
question. | [
"cs.LG",
"68T09",
"I.2.5"
] | false |
2305.08629 | 2023-05-15T13:21:50Z | A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial
Semi-Bandits, Linear Bandits, and MDPs | [
"Dirk van der Hoeven",
"Lukas Zierahn",
"Tal Lancewicki",
"Aviv Rosenberg",
"Nicoló Cesa-Bianchi"
] | We derive a new analysis of Follow The Regularized Leader (FTRL) for online
learning with delayed bandit feedback. By separating the cost of delayed
feedback from that of bandit feedback, our analysis allows us to obtain new
results in three important settings. On the one hand, we derive the first
optimal (up to logarithmic factors) regret bounds for combinatorial
semi-bandits with delay and adversarial Markov decision processes with delay
(and known transition functions). On the other hand, we use our analysis to
derive an efficient algorithm for linear bandits with delay achieving
near-optimal regret bounds. Our novel regret decomposition shows that FTRL
remains stable across multiple rounds under mild assumptions on the Hessian of
the regularizer. | [
"cs.LG"
] | false |
2305.08750 | 2023-05-15T16:02:36Z | Fast and Attributed Change Detection on Dynamic Graphs with Density of
States | [
"Shenyang Huang",
"Jacob Danovitch",
"Guillaume Rabusseau",
"Reihaneh Rabbany"
] | How can we detect traffic disturbances from international flight
transportation logs or changes to collaboration dynamics in academic networks?
These problems can be formulated as detecting anomalous change points in a
dynamic graph. Current solutions do not scale well to large real-world graphs,
lack robustness to large amounts of node additions/deletions, and overlook
changes in node attributes. To address these limitations, we propose a novel
spectral method: Scalable Change Point Detection (SCPD). SCPD generates an
embedding for each graph snapshot by efficiently approximating the distribution
of the Laplacian spectrum at each step. SCPD can also capture shifts in node
attributes by tracking correlations between attributes and eigenvectors.
Through extensive experiments using synthetic and real-world data, we show that
SCPD (a) achieves state-of-the art performance, (b) is significantly faster
than the state-of-the-art methods and can easily process millions of edges in a
few CPU minutes, (c) can effectively tackle a large quantity of node
attributes, additions or deletions and (d) discovers interesting events in
large real-world graphs. The code is publicly available at
https://github.com/shenyangHuang/SCPD.git | [
"cs.LG"
] | false |
2305.08807 | 2023-05-15T17:14:52Z | Smoothness and monotonicity constraints for neural networks using ICEnet | [
"Ronald Richman",
"Mario Wüthrich"
] | Deep neural networks have become an important tool for use in actuarial
tasks, due to the significant gains in accuracy provided by these techniques
compared to traditional methods, but also due to the close connection of these
models to the Generalized Linear Models (GLMs) currently used in industry.
Whereas constraining GLM parameters relating to insurance risk factors to be
smooth or exhibit monotonicity is trivial, methods to incorporate such
constraints into deep neural networks have not yet been developed. This is a
barrier for the adoption of neural networks in insurance practice since
actuaries often impose these constraints for commercial or statistical reasons.
In this work, we present a novel method for enforcing constraints within deep
neural network models, and we show how these models can be trained. Moreover,
we provide example applications using real-world datasets. We call our proposed
method ICEnet to emphasize the close link of our proposal to the individual
conditional expectation (ICE) model interpretability technique. | [
"cs.LG"
] | false |
2305.08813 | 2023-05-15T17:22:26Z | ReLU soothes the NTK condition number and accelerates optimization for
wide neural networks | [
"Chaoyue Liu",
"Like Hui"
] | Rectified linear unit (ReLU), as a non-linear activation function, is well
known to improve the expressivity of neural networks such that any continuous
function can be approximated to arbitrary precision by a sufficiently wide
neural network. In this work, we present another interesting and important
feature of ReLU activation function. We show that ReLU leads to: {\it better
separation} for similar data, and {\it better conditioning} of neural tangent
kernel (NTK), which are closely related. Comparing with linear neural networks,
we show that a ReLU activated wide neural network at random initialization has
a larger angle separation for similar data in the feature space of model
gradient, and has a smaller condition number for NTK. Note that, for a linear
neural network, the data separation and NTK condition number always remain the
same as in the case of a linear model. Furthermore, we show that a deeper ReLU
network (i.e., with more ReLU activation operations), has a smaller NTK
condition number than a shallower one. Our results imply that ReLU activation,
as well as the depth of ReLU network, helps improve the gradient descent
convergence rate, which is closely related to the NTK condition number. | [
"cs.LG"
] | false |
2305.08887 | 2023-05-15T03:05:57Z | Covariate-distance Weighted Regression (CWR): A Case Study for
Estimation of House Prices | [
"Hone-Jay Chu",
"Po-Hung Chen",
"Sheng-Mao Chang",
"Muhammad Zeeshan Ali",
"Sumriti Ranjan Patra"
] | Geographically weighted regression (GWR) is a popular tool for modeling
spatial heterogeneity in a regression model. However, the current weighting
function used in GWR only considers the geographical distance, while the
attribute similarity is totally ignored. In this study, we proposed a covariate
weighting function that combines the geographical distance and attribute
distance. The covariate-distance weighted regression (CWR) is the extension of
GWR including geographical distance and attribute distance. House prices are
affected by numerous factors, such as house age, floor area, and land use.
Prediction model is used to help understand the characteristics of regional
house prices. The CWR was used to understand the relationship between the house
price and controlling factors. The CWR can consider the geological and
attribute distances, and produce accurate estimates of house price that
preserve the weight matrix for geological and attribute distance functions.
Results show that the house attributes/conditions and the characteristics of
the house, such as floor area and house age, might affect the house price.
After factor selection, in which only house age and floor area of a building
are considered, the RMSE of the CWR model can be improved by 2.9%-26.3% for
skyscrapers when compared to the GWR. CWR can effectively reduce estimation
errors from traditional spatial regression models and provide novel and
feasible models for spatial estimation. | [
"cs.LG"
] | false |
2305.09018 | 2023-05-15T21:00:09Z | DATED: Guidelines for Creating Synthetic Datasets for Engineering Design
Applications | [
"Cyril Picard",
"Jürg Schiffmann",
"Faez Ahmed"
] | Exploiting the recent advancements in artificial intelligence, showcased by
ChatGPT and DALL-E, in real-world applications necessitates vast,
domain-specific, and publicly accessible datasets. Unfortunately, the scarcity
of such datasets poses a significant challenge for researchers aiming to apply
these breakthroughs in engineering design. Synthetic datasets emerge as a
viable alternative. However, practitioners are often uncertain about generating
high-quality datasets that accurately represent real-world data and are
suitable for the intended downstream applications. This study aims to fill this
knowledge gap by proposing comprehensive guidelines for generating, annotating,
and validating synthetic datasets. The trade-offs and methods associated with
each of these aspects are elaborated upon. Further, the practical implications
of these guidelines are illustrated through the creation of a turbo-compressors
dataset. The study underscores the importance of thoughtful sampling methods to
ensure the appropriate size, diversity, utility, and realism of a dataset. It
also highlights that design diversity does not equate to performance diversity
or realism. By employing test sets that represent uniform, real, or
task-specific samples, the influence of sample size and sampling strategy is
scrutinized. Overall, this paper offers valuable insights for researchers
intending to create and publish synthetic datasets for engineering design,
thereby paving the way for more effective applications of AI advancements in
the field. The code and data for the dataset and methods are made publicly
accessible at https://github.com/cyrilpic/radcomp . | [
"cs.LG"
] | false |
2305.09042 | 2023-05-15T22:04:49Z | Adaptive Federated Pruning in Hierarchical Wireless Networks | [
"Xiaonan Liu",
"Shiqiang Wang",
"Yansha Deng",
"Arumugam Nallanathan"
] | Federated Learning (FL) is a promising privacy-preserving distributed
learning framework where a server aggregates models updated by multiple devices
without accessing their private datasets. Hierarchical FL (HFL), as a
device-edge-cloud aggregation hierarchy, can enjoy both the cloud server's
access to more datasets and the edge servers' efficient communications with
devices. However, the learning latency increases with the HFL network scale due
to the increasing number of edge servers and devices with limited local
computation capability and communication bandwidth. To address this issue, in
this paper, we introduce model pruning for HFL in wireless networks to reduce
the neural network scale. We present the convergence analysis of an upper on
the l2 norm of gradients for HFL with model pruning, analyze the computation
and communication latency of the proposed model pruning scheme, and formulate
an optimization problem to maximize the convergence rate under a given latency
threshold by jointly optimizing the pruning ratio and wireless resource
allocation. By decoupling the optimization problem and using Karush Kuhn Tucker
(KKT) conditions, closed-form solutions of pruning ratio and wireless resource
allocation are derived. Simulation results show that our proposed HFL with
model pruning achieves similar learning accuracy compared with the HFL without
model pruning and reduces about 50 percent communication cost. | [
"cs.LG"
] | false |
2305.09056 | 2023-05-15T22:43:18Z | Physics-informed Convolutional Recurrent Surrogate Model for Reservoir
Simulation with Well Controls | [
"Jungang Chen",
"Eduardo Gildin",
"John E. Killough"
] | This paper presents a novel surrogate model for modeling subsurface fluid
flow with well controls using a physics-informed convolutional recurrent neural
network (PICRNN). The model uses a convolutional long-short term memory
(ConvLSTM) to capture the spatiotemporal dependencies of the state evolution
dynamics in the porous flow. The ConvLSTM is linked to the state space
equations, enabling the incorporation of a discrete-time sequence of well
control. The model requires initial state condition and a sequence of well
controls as inputs, and predicts the state variables of the system, such as
pressure, as output. By minimizing the residuals of reservoir flow state-space
equations, the network is trained without the need for labeled data. The model
is designed to serve as a surrogate model for predicting future reservoir
states based on the initial reservoir state and input engineering controls.
Boundary conditions are enforced into the state-space equations so no
additional loss term is needed. Three numerical cases are studied,
demonstrating the model's effectiveness in predicting reservoir dynamics based
on future well/system controls. The proposed model provides a new approach for
efficient and accurate prediction of subsurface fluid flow, with potential
applications in optimal control design for reservoir engineering. | [
"cs.LG"
] | false |
2305.09060 | 2023-05-15T23:00:25Z | Learning Linear Embeddings for Non-Linear Network Dynamics with Koopman
Message Passing | [
"King Fai Yeh",
"Paris Flood",
"William Redman",
"Pietro Liò"
] | Recently, Koopman operator theory has become a powerful tool for developing
linear representations of non-linear dynamical systems. However, existing
data-driven applications of Koopman operator theory, including both traditional
and deep learning approaches, perform poorly on non-linear network dynamics
problems as they do not address the underlying geometric structure. In this
paper we present a novel approach based on Koopman operator theory and message
passing networks that finds a linear representation for the dynamical system
which is globally valid at any time step. The linearisations found by our
method produce predictions on a suite of network dynamics problems that are
several orders of magnitude better than current state-of-the-art techniques. We
also apply our approach to the highly non-linear training dynamics of neural
network architectures, and obtain linear representations which can generate
network parameters with comparable performance to networks trained by classical
optimisers. | [
"cs.LG"
] | false |
2305.09063 | 2023-05-15T23:12:15Z | Bounded KRnet and its applications to density estimation and
approximation | [
"Li Zeng",
"Xiaoliang Wan",
"Tao Zhou"
] | In this paper, we develop an invertible mapping, called B-KRnet, on a bounded
domain and apply it to density estimation/approximation for data or the
solutions of PDEs such as the Fokker-Planck equation and the Keller-Segel
equation. Similar to KRnet, the structure of B-KRnet adapts the triangular form
of the Knothe-Rosenblatt rearrangement into a normalizing flow model. The main
difference between B-KRnet and KRnet is that B-KRnet is defined on a hypercube
while KRnet is defined on the whole space, in other words, we introduce a new
mechanism in B-KRnet to maintain the exact invertibility. Using B-KRnet as a
transport map, we obtain an explicit probability density function (PDF) model
that corresponds to the pushforward of a prior (uniform) distribution on the
hypercube. To approximate PDFs defined on a bounded computational domain,
B-KRnet is more effective than KRnet. By coupling KRnet and B-KRnet, we can
also define a deep generative model on a high-dimensional domain where some
dimensions are bounded and other dimensions are unbounded. A typical case is
the solution of the stationary kinetic Fokker-Planck equation, which is a PDF
of position and momentum. Based on B-KRnet, we develop an adaptive learning
approach to approximate partial differential equations whose solutions are PDFs
or can be regarded as a PDF. In addition, we apply B-KRnet to density
estimation when only data are available. A variety of numerical experiments is
presented to demonstrate the effectiveness of B-KRnet. | [
"cs.LG"
] | false |
2305.09070 | 2023-05-15T23:51:07Z | An Offline Time-aware Apprenticeship Learning Framework for Evolving
Reward Functions | [
"Xi Yang",
"Ge Gao",
"Min Chi"
] | Apprenticeship learning (AL) is a process of inducing effective
decision-making policies via observing and imitating experts' demonstrations.
Most existing AL approaches, however, are not designed to cope with the
evolving reward functions commonly found in human-centric tasks such as
healthcare, where offline learning is required. In this paper, we propose an
offline Time-aware Hierarchical EM Energy-based Sub-trajectory (THEMES) AL
framework to tackle the evolving reward functions in such tasks. The
effectiveness of THEMES is evaluated via a challenging task -- sepsis
treatment. The experimental results demonstrate that THEMES can significantly
outperform competitive state-of-the-art baselines. | [
"cs.LG"
] | false |
2305.08328 | 2023-05-15T03:34:42Z | FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical
Federated Learning | [
"Penghui Wei",
"Hongjian Dou",
"Shaoguo Liu",
"Rongjun Tang",
"Li Liu",
"Liang Wang",
"Bo Zheng"
] | Conversion rate (CVR) estimation aims to predict the probability of
conversion event after a user has clicked an ad. Typically, online publisher
has user browsing interests and click feedbacks, while demand-side advertising
platform collects users' post-click behaviors such as dwell time and conversion
decisions. To estimate CVR accurately and protect data privacy better, vertical
federated learning (vFL) is a natural solution to combine two sides' advantages
for training models, without exchanging raw data. Both CVR estimation and
applied vFL algorithms have attracted increasing research attentions. However,
standardized and systematical evaluations are missing: due to the lack of
standardized datasets, existing studies adopt public datasets to simulate a vFL
setting via hand-crafted feature partition, which brings challenges to fair
comparison. We introduce FedAds, the first benchmark for CVR estimation with
vFL, to facilitate standardized and systematical evaluations for vFL
algorithms. It contains a large-scale real world dataset collected from
Alibaba's advertising platform, as well as systematical evaluations for both
effectiveness and privacy aspects of various vFL algorithms. Besides, we also
explore to incorporate unaligned data in vFL to improve effectiveness, and
develop perturbation operations to protect privacy well. We hope that future
research work in vFL and CVR estimation benefits from the FedAds benchmark. | [
"cs.IR",
"cs.LG"
] | false |
2305.08457 | 2023-05-15T08:59:35Z | MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation | [
"Yiheng Zhu",
"Zhenqiu Ouyang",
"Ben Liao",
"Jialu Wu",
"Yixuan Wu",
"Chang-Yu Hsieh",
"Tingjun Hou",
"Jian Wu"
] | Molecular de novo design is a critical yet challenging task in scientific
fields, aiming to design novel molecular structures with desired property
profiles. Significant progress has been made by resorting to generative models
for graphs. However, limited attention is paid to hierarchical generative
models, which can exploit the inherent hierarchical structure (with rich
semantic information) of the molecular graphs and generate complex molecules of
larger size that we shall demonstrate to be difficult for most existing models.
The primary challenge to hierarchical generation is the non-differentiable
issue caused by the generation of intermediate discrete coarsened graph
structures. To sidestep this issue, we cast the tricky hierarchical generation
problem over discrete spaces as the reverse process of hierarchical
representation learning and propose MolHF, a new hierarchical flow-based model
that generates molecular graphs in a coarse-to-fine manner. Specifically, MolHF
first generates bonds through a multi-scale architecture, then generates atoms
based on the coarsened graph structure at each scale. We demonstrate that MolHF
achieves state-of-the-art performance in random generation and property
optimization, implying its high capacity to model data distribution.
Furthermore, MolHF is the first flow-based model that can be applied to model
larger molecules (polymer) with more than 100 heavy atoms. The code and models
are available at https://github.com/violet-sto/MolHF. | [
"cs.LG",
"stat.ML"
] | false |
2305.08501 | 2023-05-15T09:57:04Z | Label Smoothing is Robustification against Model Misspecification | [
"Ryoya Yamasaki",
"Toshiyuki Tanaka"
] | Label smoothing (LS) adopts smoothed targets in classification tasks. For
example, in binary classification, instead of the one-hot target $(1,0)^\top$
used in conventional logistic regression (LR), LR with LS (LSLR) uses the
smoothed target $(1-\frac{\alpha}{2},\frac{\alpha}{2})^\top$ with a smoothing
level $\alpha\in(0,1)$, which causes squeezing of values of the logit. Apart
from the common regularization-based interpretation of LS that leads to an
inconsistent probability estimator, we regard LSLR as modifying the loss
function and consistent estimator for probability estimation. In order to study
the significance of each of these two modifications by LSLR, we introduce a
modified LSLR (MLSLR) that uses the same loss function as LSLR and the same
consistent estimator as LR, while not squeezing the logits. For the loss
function modification, we theoretically show that MLSLR with a larger smoothing
level has lower efficiency with correctly-specified models, while it exhibits
higher robustness against model misspecification than LR. Also, for the
modification of the probability estimator, an experimental comparison between
LSLR and MLSLR showed that this modification and squeezing of the logits in
LSLR have negative effects on the probability estimation and classification
performance. The understanding of the properties of LS provided by these
comparisons allows us to propose MLSLR as an improvement over LSLR. | [
"stat.ML",
"cs.LG"
] | false |
2305.08506 | 2023-05-15T10:14:30Z | A Knowledge Graph Perspective on Supply Chain Resilience | [
"Yushan Liu",
"Bailan He",
"Marcel Hildebrandt",
"Maximilian Buchner",
"Daniela Inzko",
"Roger Wernert",
"Emanuel Weigel",
"Dagmar Beyer",
"Martin Berbalk",
"Volker Tresp"
] | Global crises and regulatory developments require increased supply chain
transparency and resilience. Companies do not only need to react to a dynamic
environment but have to act proactively and implement measures to prevent
production delays and reduce risks in the supply chains. However, information
about supply chains, especially at the deeper levels, is often intransparent
and incomplete, making it difficult to obtain precise predictions about
prospective risks. By connecting different data sources, we model the supply
network as a knowledge graph and achieve transparency up to tier-3 suppliers.
To predict missing information in the graph, we apply state-of-the-art
knowledge graph completion methods and attain a mean reciprocal rank of 0.4377
with the best model. Further, we apply graph analysis algorithms to identify
critical entities in the supply network, supporting supply chain managers in
automated risk identification. | [
"cs.LG",
"cs.AI"
] | false |
2305.08733 | 2023-05-15T15:47:19Z | Refining Amortized Posterior Approximations using Gradient-Based Summary
Statistics | [
"Rafael Orozco",
"Ali Siahkoohi",
"Mathias Louboutin",
"Felix J. Herrmann"
] | We present an iterative framework to improve the amortized approximations of
posterior distributions in the context of Bayesian inverse problems, which is
inspired by loop-unrolled gradient descent methods and is theoretically
grounded in maximally informative summary statistics. Amortized variational
inference is restricted by the expressive power of the chosen variational
distribution and the availability of training data in the form of joint data
and parameter samples, which often lead to approximation errors such as the
amortization gap. To address this issue, we propose an iterative framework that
refines the current amortized posterior approximation at each step. Our
approach involves alternating between two steps: (1) constructing a training
dataset consisting of pairs of summarized data residuals and parameters, where
the summarized data residual is generated using a gradient-based summary
statistic, and (2) training a conditional generative model -- a normalizing
flow in our examples -- on this dataset to obtain a probabilistic update of the
unknown parameter. This procedure leads to iterative refinement of the
amortized posterior approximations without the need for extra training data. We
validate our method in a controlled setting by applying it to a stylized
problem, and observe improved posterior approximations with each iteration.
Additionally, we showcase the capability of our method in tackling
realistically sized problems by applying it to transcranial ultrasound, a
high-dimensional, nonlinear inverse problem governed by wave physics, and
observe enhanced posterior quality through better image reconstruction with the
posterior mean. | [
"cs.LG",
"physics.data-an"
] | false |
2305.08753 | 2023-05-15T16:05:58Z | Neural Oscillators are Universal | [
"Samuel Lanthaler",
"T. Konstantin Rusch",
"Siddhartha Mishra"
] | Coupled oscillators are being increasingly used as the basis of machine
learning (ML) architectures, for instance in sequence modeling, graph
representation learning and in physical neural networks that are used in analog
ML devices. We introduce an abstract class of neural oscillators that
encompasses these architectures and prove that neural oscillators are
universal, i.e, they can approximate any continuous and casual operator mapping
between time-varying functions, to desired accuracy. This universality result
provides theoretical justification for the use of oscillator based ML systems.
The proof builds on a fundamental result of independent interest, which shows
that a combination of forced harmonic oscillators with a nonlinear read-out
suffices to approximate the underlying operators. | [
"cs.NE",
"cs.LG"
] | false |
2305.08767 | 2023-05-15T16:26:03Z | DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load
Forecasting with LSTM Networks | [
"Firas Bayram",
"Phil Aupke",
"Bestoun S. Ahmed",
"Andreas Kassler",
"Andreas Theocharis",
"Jonas Forsman"
] | Load forecasting is a crucial topic in energy management systems (EMS) due to
its vital role in optimizing energy scheduling and enabling more flexible and
intelligent power grid systems. As a result, these systems allow power utility
companies to respond promptly to demands in the electricity market. Deep
learning (DL) models have been commonly employed in load forecasting problems
supported by adaptation mechanisms to cope with the changing pattern of
consumption by customers, known as concept drift. A drift magnitude threshold
should be defined to design change detection methods to identify drifts. While
the drift magnitude in load forecasting problems can vary significantly over
time, existing literature often assumes a fixed drift magnitude threshold,
which should be dynamically adjusted rather than fixed during system evolution.
To address this gap, in this paper, we propose a dynamic drift-adaptive Long
Short-Term Memory (DA-LSTM) framework that can improve the performance of load
forecasting models without requiring a drift threshold setting. We integrate
several strategies into the framework based on active and passive adaptation
approaches. To evaluate DA-LSTM in real-life settings, we thoroughly analyze
the proposed framework and deploy it in a real-world problem through a
cloud-based environment. Efficiency is evaluated in terms of the prediction
performance of each approach and computational cost. The experiments show
performance improvements on multiple evaluation metrics achieved by our
framework compared to baseline methods from the literature. Finally, we present
a trade-off analysis between prediction performance and computational costs. | [
"cs.LG",
"cs.AI"
] | false |
2305.08791 | 2023-05-15T16:51:18Z | Fair Information Spread on Social Networks with Community Structure | [
"Octavio Mesner",
"Elizaveta Levina",
"Ji Zhu"
] | Information spread through social networks is ubiquitous. Influence maximiza-
tion (IM) algorithms aim to identify individuals who will generate the greatest
spread through the social network if provided with information, and have been
largely devel- oped with marketing in mind. In social networks with community
structure, which are very common, IM algorithms focused solely on maximizing
spread may yield signifi- cant disparities in information coverage between
communities, which is problematic in settings such as public health messaging.
While some IM algorithms aim to remedy disparity in information coverage using
node attributes, none use the empirical com- munity structure within the
network itself, which may be beneficial since communities directly affect the
spread of information. Further, the use of empirical network struc- ture allows
us to leverage community detection techniques, making it possible to run
fair-aware algorithms when there are no relevant node attributes available, or
when node attributes do not accurately capture network community structure. In
contrast to other fair IM algorithms, this work relies on fitting a model to
the social network which is then used to determine a seed allocation strategy
for optimal fair information spread. We develop an algorithm to determine
optimal seed allocations for expected fair coverage, defined through maximum
entropy, provide some theoretical guarantees under appropriate conditions, and
demonstrate its empirical accuracy on both simu- lated and real networks.
Because this algorithm relies on a fitted network model and not on the network
directly, it is well-suited for partially observed and noisy social networks. | [
"stat.ML",
"cs.LG"
] | false |
2305.08796 | 2023-05-15T17:00:23Z | Predictive Models from Quantum Computer Benchmarks | [
"Daniel Hothem",
"Jordan Hines",
"Karthik Nataraj",
"Robin Blume-Kohout",
"Timothy Proctor"
] | Holistic benchmarks for quantum computers are essential for testing and
summarizing the performance of quantum hardware. However, holistic benchmarks
-- such as algorithmic or randomized benchmarks -- typically do not predict a
processor's performance on circuits outside the benchmark's necessarily very
limited set of test circuits. In this paper, we introduce a general framework
for building predictive models from benchmarking data using capability models.
Capability models can be fit to many kinds of benchmarking data and used for a
variety of predictive tasks. We demonstrate this flexibility with two case
studies. In the first case study, we predict circuit (i) process fidelities and
(ii) success probabilities by fitting error rates models to two kinds of
volumetric benchmarking data. Error rates models are simple, yet versatile
capability models which assign effective error rates to individual gates, or
more general circuit components. In the second case study, we construct a
capability model for predicting circuit success probabilities by applying
transfer learning to ResNet50, a neural network trained for image
classification. Our case studies use data from cloud-accessible quantum
computers and simulations of noisy quantum computers. | [
"quant-ph",
"cs.LG"
] | false |
2305.08819 | 2023-05-15T17:30:05Z | Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its
High-Performance CUDA Library | [
"Zhiyi Zhang",
"Pengfei Zhang",
"Qi Wang"
] | Java is very powerful, but in Deep Learning field, its capabilities probably
has not been sufficiently exploited. Compared to the Java-based
deep-learning-frameworks, the Python-based (PyTorch, TensorFlow, etc) are
undoubtedly the mainstream, due to their easy-to-use, flexibility and better
ecosystem. Dragon-Alpha is a Java-based Tensor Computing Framework, with
easy-to-use, high-scalability and high-performance, trying to break Java's
dilemma in deep learning field and make it more effective. Dragon-Alpha
supports different levels of APIs, and can be used as a deep-learning-framework
through its user-friendly high-level APIs. Dragon-Alpha has potential to
aggregate computing-power across heterogeneous platforms and devices, based on
its multi-layer architecture and Java's big-data ecosystem. Dragon-Alpha has
its asynchronized APIs to improve parallelism, and highly-optimized CUDA
library cu32 which adopts unique convolution\deconvolution operators for small
feature maps. The experiments show that, compared to PyTorch&cuDNN,
Dragon-Alpha&cu32 costs less time and memory (75.38% to 97.32%, 29.2% to
66.4%), to train some typical neural networks (AlexNet, VGG, GoogleNet, ResNet)
on Cifar-10. | [
"cs.LG",
"cs.SE"
] | false |
2305.08842 | 2023-05-15T17:56:36Z | Straightening Out the Straight-Through Estimator: Overcoming
Optimization Challenges in Vector Quantized Networks | [
"Minyoung Huh",
"Brian Cheung",
"Pulkit Agrawal",
"Phillip Isola"
] | This work examines the challenges of training neural networks using vector
quantization using straight-through estimation. We find that a primary cause of
training instability is the discrepancy between the model embedding and the
code-vector distribution. We identify the factors that contribute to this
issue, including the codebook gradient sparsity and the asymmetric nature of
the commitment loss, which leads to misaligned code-vector assignments. We
propose to address this issue via affine re-parameterization of the code
vectors. Additionally, we introduce an alternating optimization to reduce the
gradient error introduced by the straight-through estimation. Moreover, we
propose an improvement to the commitment loss to ensure better alignment
between the codebook representation and the model embedding. These optimization
methods improve the mathematical approximation of the straight-through
estimation and, ultimately, the model performance. We demonstrate the
effectiveness of our methods on several common model architectures, such as
AlexNet, ResNet, and ViT, across various tasks, including image classification
and generative modeling. | [
"cs.LG",
"cs.AI"
] | false |
2305.08852 | 2023-05-15T17:59:34Z | Python Tool for Visualizing Variability of Pareto Fronts over Multiple
Runs | [
"Shuhei Watanabe"
] | Hyperparameter optimization is crucial to achieving high performance in deep
learning. On top of the performance, other criteria such as inference time or
memory requirement often need to be optimized due to some practical reasons.
This motivates research on multi-objective optimization (MOO). However, Pareto
fronts of MOO methods are often shown without considering the variability
caused by random seeds and this makes the performance stability evaluation
difficult. Although there is a concept named empirical attainment surface to
enable the visualization with uncertainty over multiple runs, there is no major
Python package for empirical attainment surface. We, therefore, develop a
Python package for this purpose and describe the usage. The package is
available at https://github.com/nabenabe0928/empirical-attainment-func. | [
"cs.AI",
"cs.LG"
] | false |
2305.08889 | 2023-05-15T09:51:30Z | New methods for new data? An overview and illustration of quantitative
inductive methods for HRM research | [
"Alain LACROUX"
] | "Data is the new oil", in short, data would be the essential source of the
ongoing fourth industrial revolution, which has led some commentators to
assimilate too quickly the quantity of data to a source of wealth in itself,
and consider the development of big data as an quasi direct cause of profit.
Human resources management is not escaping this trend, and the accumulation of
large amounts of data on employees is perceived by some entrepreneurs as a
necessary and sufficient condition for the construction of predictive models of
complex work behaviors such as absenteeism or job performance. In fact, the
analogy is somewhat misleading: unlike oil, there are no major issues here
concerning the production of data (whose flows are generated continuously and
at low cost by various information systems), but rather their ''refining'',
i.e. the operations necessary to transform this data into a useful product,
namely into knowledge. This transformation is where the methodological
challenges of data valuation lie, both for practitioners and for academic
researchers. Considerations on the methods applicable to take advantage of the
possibilities offered by these massive data are relatively recent, and often
highlight the disruptive aspect of the current ''data deluge'' to point out
that this evolution would be the source of a revival of empiricism in a
''fourth paradigm'' based on the intensive and ''agnostic'' exploitation of
massive amounts of data in order to bring out new knowledge, following a purely
inductive logic. Although we do not adopt this speculative point of view, it is
clear that data-driven approaches are scarce in quantitative HRM studies.
However, there are well-established methods, particularly in the field of data
mining, which are based on inductive approaches. This area of quantitative
analysis with an inductive aim is still relatively unexplored in HRM ( apart
from typological analyses). The objective of this paper is first to give an
overview of data driven methods that can be used for HRM research, before
proposing an empirical illustration which consists in an exploratory research
combining a latent profile analysis and an exploration by Gaussian graphical
models. | [
"cs.LG",
"stat.ME"
] | false |
2305.08932 | 2023-05-15T18:08:28Z | MIMEx: Intrinsic Rewards from Masked Input Modeling | [
"Toru Lin",
"Allan Jabri"
] | Exploring in environments with high-dimensional observations is hard. One
promising approach for exploration is to use intrinsic rewards, which often
boils down to estimating "novelty" of states, transitions, or trajectories with
deep networks. Prior works have shown that conditional prediction objectives
such as masked autoencoding can be seen as stochastic estimation of
pseudo-likelihood. We show how this perspective naturally leads to a unified
view on existing intrinsic reward approaches: they are special cases of
conditional prediction, where the estimation of novelty can be seen as
pseudo-likelihood estimation with different mask distributions. From this view,
we propose a general framework for deriving intrinsic rewards -- Masked Input
Modeling for Exploration (MIMEx) -- where the mask distribution can be flexibly
tuned to control the difficulty of the underlying conditional prediction task.
We demonstrate that MIMEx can achieve superior results when compared against
competitive baselines on a suite of challenging sparse-reward visuomotor tasks. | [
"cs.LG",
"cs.AI"
] | false |
2305.09044 | 2023-05-15T22:08:47Z | Scalable and Robust Tensor Ring Decomposition for Large-scale Data | [
"Yicong He",
"George K. Atia"
] | Tensor ring (TR) decomposition has recently received increased attention due
to its superior expressive performance for high-order tensors. However, the
applicability of traditional TR decomposition algorithms to real-world
applications is hindered by prevalent large data sizes, missing entries, and
corruption with outliers. In this work, we propose a scalable and robust TR
decomposition algorithm capable of handling large-scale tensor data with
missing entries and gross corruptions. We first develop a novel auto-weighted
steepest descent method that can adaptively fill the missing entries and
identify the outliers during the decomposition process. Further, taking
advantage of the tensor ring model, we develop a novel fast Gram matrix
computation (FGMC) approach and a randomized subtensor sketching (RStS)
strategy which yield significant reduction in storage and computational
complexity. Experimental results demonstrate that the proposed method
outperforms existing TR decomposition methods in the presence of outliers, and
runs significantly faster than existing robust tensor completion algorithms. | [
"cs.LG",
"stat.ML"
] | false |
2305.09057 | 2023-05-15T22:53:12Z | Self-Supervised Pretraining on Paired Sequences of fMRI Data for
Transfer Learning to Brain Decoding Tasks | [
"Sean Paulsen",
"Michael Casey"
] | In this work we introduce a self-supervised pretraining framework for
transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we
pretrain our architecture on two self-supervised tasks simultaneously to teach
the model a general understanding of the temporal and spatial dynamics of human
auditory cortex during music listening. Our pretraining results are the first
to suggest a synergistic effect of multitask training on fMRI data. Second, we
finetune the pretrained models and train additional fresh models on a
supervised fMRI classification task. We observe significantly improved accuracy
on held-out runs with the finetuned models, which demonstrates the ability of
our pretraining tasks to facilitate transfer learning. This work contributes to
the growing body of literature on transformer architectures for pretraining and
transfer learning with fMRI data, and serves as a proof of concept for our
pretraining tasks and multitask pretraining on fMRI data. | [
"cs.LG",
"q-bio.NC"
] | false |
2305.09071 | 2023-05-15T23:53:59Z | FiMReSt: Finite Mixture of Multivariate Regulated Skew-t Kernels -- A
Flexible Probabilistic Model for Multi-Clustered Data with
Asymmetrically-Scattered Non-Gaussian Kernels | [
"Sarmad Mehrdad",
"S. Farokh Atashzar"
] | Recently skew-t mixture models have been introduced as a flexible
probabilistic modeling technique taking into account both skewness in data
clusters and the statistical degree of freedom (S-DoF) to improve modeling
generalizability, and robustness to heavy tails and skewness. In this paper, we
show that the state-of-the-art skew-t mixture models fundamentally suffer from
a hidden phenomenon named here as "S-DoF explosion," which results in local
minima in the shapes of normal kernels during the non-convex iterative process
of expectation maximization. For the first time, this paper provides insights
into the instability of the S-DoF, which can result in the divergence of the
kernels from the mixture of t-distribution, losing generalizability and power
for modeling the outliers. Thus, in this paper, we propose a regularized
iterative optimization process to train the mixture model, enhancing the
generalizability and resiliency of the technique. The resulting mixture model
is named Finite Mixture of Multivariate Regulated Skew-t (FiMReSt) Kernels,
which stabilizes the S-DoF profile during optimization process of learning. To
validate the performance, we have conducted a comprehensive experiment on
several real-world datasets and a synthetic dataset. The results highlight (a)
superior performance of the FiMReSt, (b) generalizability in the presence of
outliers, and (c) convergence of S-DoF. | [
"cs.LG",
"eess.SP"
] | false |
2305.17137 | 2023-05-15T09:09:40Z | Integrating Generative Artificial Intelligence in Intelligent Vehicle
Systems | [
"Lukas Stappen",
"Jeremy Dillmann",
"Serena Striegel",
"Hans-Jörg Vögel",
"Nicolas Flores-Herr",
"Björn W. Schuller"
] | This paper aims to serve as a comprehensive guide for researchers and
practitioners, offering insights into the current state, potential
applications, and future research directions for generative artificial
intelligence and foundation models within the context of intelligent vehicles.
As the automotive industry progressively integrates AI, generative artificial
intelligence technologies hold the potential to revolutionize user
interactions, delivering more immersive, intuitive, and personalised in-car
experiences. We provide an overview of current applications of generative
artificial intelligence in the automotive domain, emphasizing speech, audio,
vision, and multimodal interactions. We subsequently outline critical future
research areas, including domain adaptability, alignment, multimodal
integration and others, as well as, address the challenges and risks associated
with ethics. By fostering collaboration and addressing these research areas,
generative artificial intelligence can unlock its full potential, transforming
the driving experience and shaping the future of intelligent vehicles. | [
"cs.AI",
"cs.LG"
] | false |
2305.08303 | 2023-05-15T02:13:41Z | Deep-Unfolding for Next-Generation Transceivers | [
"Qiyu Hu",
"Yunlong Cai",
"Guangyi Zhang",
"Guanding Yu",
"Geoffrey Ye Li"
] | The stringent performance requirements of future wireless networks, such as
ultra-high data rates, extremely high reliability and low latency, are spurring
worldwide studies on defining the next-generation multiple-input
multiple-output (MIMO) transceivers. For the design of advanced transceivers in
wireless communications, optimization approaches often leading to iterative
algorithms have achieved great success for MIMO transceivers. However, these
algorithms generally require a large number of iterations to converge, which
entails considerable computational complexity and often requires fine-tuning of
various parameters. With the development of deep learning, approximating the
iterative algorithms with deep neural networks (DNNs) can significantly reduce
the computational time. However, DNNs typically lead to black-box solvers,
which requires amounts of data and extensive training time. To further overcome
these challenges, deep-unfolding has emerged which incorporates the benefits of
both deep learning and iterative algorithms, by unfolding the iterative
algorithm into a layer-wise structure analogous to DNNs. In this article, we
first go through the framework of deep-unfolding for transceiver design with
matrix parameters and its recent advancements. Then, some endeavors in applying
deep-unfolding approaches in next-generation advanced transceiver design are
presented. Moreover, some open issues for future research are highlighted. | [
"eess.SP",
"cs.IT",
"cs.LG",
"math.IT"
] | false |
2305.08316 | 2023-05-15T03:06:44Z | SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient
and Generalizable Protein-Protein Interaction Prediction | [
"Ziyuan Zhao",
"Peisheng Qian",
"Xulei Yang",
"Zeng Zeng",
"Cuntai Guan",
"Wai Leong Tam",
"Xiaoli Li"
] | Protein-protein interactions (PPIs) are crucial in various biological
processes and their study has significant implications for drug development and
disease diagnosis. Existing deep learning methods suffer from significant
performance degradation under complex real-world scenarios due to various
factors, e.g., label scarcity and domain shift. In this paper, we propose a
self-ensembling multigraph neural network (SemiGNN-PPI) that can effectively
predict PPIs while being both efficient and generalizable. In SemiGNN-PPI, we
not only model the protein correlations but explore the label dependencies by
constructing and processing multiple graphs from the perspectives of both
features and labels in the graph learning process. We further marry GNN with
Mean Teacher to effectively leverage unlabeled graph-structured PPI data for
self-ensemble graph learning. We also design multiple graph consistency
constraints to align the student and teacher graphs in the feature embedding
space, enabling the student model to better learn from the teacher model by
incorporating more relationships. Extensive experiments on PPI datasets of
different scales with different evaluation settings demonstrate that
SemiGNN-PPI outperforms state-of-the-art PPI prediction methods, particularly
in challenging scenarios such as training with limited annotations and testing
on unseen data. | [
"q-bio.MN",
"cs.AI",
"cs.CE",
"cs.LG"
] | false |
2305.08337 | 2023-05-15T04:03:51Z | Neural Boltzmann Machines | [
"Alex H. Lang",
"Anton D. Loukianov",
"Charles K. Fisher"
] | Conditional generative models are capable of using contextual information as
input to create new imaginative outputs. Conditional Restricted Boltzmann
Machines (CRBMs) are one class of conditional generative models that have
proven to be especially adept at modeling noisy discrete or continuous data,
but the lack of expressivity in CRBMs have limited their widespread adoption.
Here we introduce Neural Boltzmann Machines (NBMs) which generalize CRBMs by
converting each of the CRBM parameters to their own neural networks that are
allowed to be functions of the conditional inputs. NBMs are highly flexible
conditional generative models that can be trained via stochastic gradient
descent to approximately maximize the log-likelihood of the data. We
demonstrate the utility of NBMs especially with normally distributed data which
has historically caused problems for Gaussian-Bernoulli CRBMs. Code to
reproduce our results can be found at
https://github.com/unlearnai/neural-boltzmann-machines. | [
"cs.LG",
"cond-mat.dis-nn",
"stat.ML"
] | false |
2305.08350 | 2023-05-15T05:07:45Z | Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension | [
"Yue Wu",
"Jiafan He",
"Quanquan Gu"
] | Recently, there has been remarkable progress in reinforcement learning (RL)
with general function approximation. However, all these works only provide
regret or sample complexity guarantees. It is still an open question if one can
achieve stronger performance guarantees, i.e., the uniform probably approximate
correctness (Uniform-PAC) guarantee that can imply both a sub-linear regret
bound and a polynomial sample complexity for any target learning accuracy. We
study this problem by proposing algorithms for both nonlinear bandits and
model-based episodic RL using the general function class with a bounded eluder
dimension. The key idea of the proposed algorithms is to assign each action to
different levels according to its width with respect to the confidence set. The
achieved uniform-PAC sample complexity is tight in the sense that it matches
the state-of-the-art regret bounds or sample complexity guarantees when reduced
to the linear case. To the best of our knowledge, this is the first work for
uniform-PAC guarantees on bandit and RL that goes beyond linear cases. | [
"cs.LG",
"math.OC",
"stat.ML"
] | false |
2305.08359 | 2023-05-15T05:37:32Z | Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs | [
"Kaixuan Ji",
"Qingyue Zhao",
"Jiafan He",
"Weitong Zhang",
"Quanquan Gu"
] | Recent studies have shown that episodic reinforcement learning (RL) is no
harder than bandits when the total reward is bounded by $1$, and proved regret
bounds that have a polylogarithmic dependence on the planning horizon $H$.
However, it remains an open question that if such results can be carried over
to adversarial RL, where the reward is adversarially chosen at each episode. In
this paper, we answer this question affirmatively by proposing the first
horizon-free policy search algorithm. To tackle the challenges caused by
exploration and adversarially chosen reward, our algorithm employs (1) a
variance-uncertainty-aware weighted least square estimator for the transition
kernel; and (2) an occupancy measure-based technique for the online search of a
\emph{stochastic} policy. We show that our algorithm achieves an
$\tilde{O}\big((d+\log (|\mathcal{S}|^2 |\mathcal{A}|))\sqrt{K}\big)$ regret
with full-information feedback, where $d$ is the dimension of a known feature
mapping linearly parametrizing the unknown transition kernel of the MDP, $K$ is
the number of episodes, $|\mathcal{S}|$ and $|\mathcal{A}|$ are the
cardinalities of the state and action spaces. We also provide hardness results
and regret lower bounds to justify the near optimality of our algorithm and the
unavoidability of $\log|\mathcal{S}|$ and $\log|\mathcal{A}|$ in the regret
bound. | [
"cs.LG",
"math.OC",
"stat.ML"
] | false |
2305.08466 | 2023-05-15T09:10:12Z | Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural
Network Derivatives | [
"Yahong Yang",
"Haizhao Yang",
"Yang Xiang"
] | This paper addresses the problem of nearly optimal Vapnik--Chervonenkis
dimension (VC-dimension) and pseudo-dimension estimations of the derivative
functions of deep neural networks (DNNs). Two important applications of these
estimations include: 1) Establishing a nearly tight approximation result of
DNNs in the Sobolev space; 2) Characterizing the generalization error of
machine learning methods with loss functions involving function derivatives.
This theoretical investigation fills the gap of learning error estimations for
a wide range of physics-informed machine learning models and applications
including generative models, solving partial differential equations, operator
learning, network compression, distillation, regularization, etc. | [
"cs.LG",
"cs.NA",
"math.NA"
] | false |
2305.08481 | 2023-05-15T09:32:42Z | Task-Oriented Communication Design at Scale | [
"Arsham Mostaani",
"Thang X. Vu",
"Hamed Habibi",
"Symeon Chatzinotas",
"Bjorn Ottersten"
] | With countless promising applications in various domains such as IoT and
industry 4.0, task-oriented communication design (TOCD) is getting accelerated
attention from the research community. This paper presents a novel approach for
designing scalable task-oriented quantization and communications in cooperative
multi-agent systems (MAS). The proposed approach utilizes the TOCD framework
and the value of information (VoI) concept to enable efficient communication of
quantized observations among agents while maximizing the average return
performance of the MAS, a parameter that quantifies the MAS's task
effectiveness. The computational complexity of learning the VoI, however, grows
exponentially with the number of agents. Thus, we propose a three-step
framework: i) learning the VoI (using reinforcement learning (RL)) for a
two-agent system, ii) designing the quantization policy for an $N$-agent MAS
using the learned VoI for a range of bit-budgets and, (iii) learning the
agents' control policies using RL while following the designed quantization
policies in the earlier step. We observe that one can reduce the computational
cost of obtaining the value of information by exploiting insights gained from
studying a similar two-agent system - instead of the original $N$-agent system.
We then quantize agents' observations such that their more valuable
observations are communicated more precisely. Our analytical results show the
applicability of the proposed framework under a wide range of problems.
Numerical results show striking improvements in reducing the computational
complexity of obtaining VoI needed for the TOCD in a MAS problem without
compromising the average return performance of the MAS. | [
"cs.IT",
"cs.LG",
"cs.MA",
"math.IT"
] | false |
2305.08504 | 2023-05-15T10:09:07Z | FLARE: Detection and Mitigation of Concept Drift for Federated Learning
based IoT Deployments | [
"Theo Chow",
"Usman Raza",
"Ioannis Mavromatis",
"Aftab Khan"
] | Intelligent, large-scale IoT ecosystems have become possible due to recent
advancements in sensing technologies, distributed learning, and low-power
inference in embedded devices. In traditional cloud-centric approaches, raw
data is transmitted to a central server for training and inference purposes. On
the other hand, Federated Learning migrates both tasks closer to the edge nodes
and endpoints. This allows for a significant reduction in data exchange while
preserving the privacy of users. Trained models, though, may under-perform in
dynamic environments due to changes in the data distribution, affecting the
model's ability to infer accurately; this is referred to as concept drift. Such
drift may also be adversarial in nature. Therefore, it is of paramount
importance to detect such behaviours promptly. In order to simultaneously
reduce communication traffic and maintain the integrity of inference models, we
introduce FLARE, a novel lightweight dual-scheduler FL framework that
conditionally transfers training data, and deploys models between edge and
sensor endpoints based on observing the model's training behaviour and
inference statistics, respectively. We show that FLARE can significantly reduce
the amount of data exchanged between edge and sensor nodes compared to
fixed-interval scheduling methods (over 5x reduction), is easily scalable to
larger systems, and can successfully detect concept drift reactively with at
least a 16x reduction in latency. | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.NI"
] | false |
2305.08624 | 2023-05-15T13:19:03Z | Mastering the exploration-exploitation trade-off in Bayesian
Optimization | [
"Antonio Candelieri"
] | Gaussian Process based Bayesian Optimization is a well-known sample efficient
sequential strategy for globally optimizing black-box, expensive, and
multi-extremal functions. The role of the Gaussian Process is to provide a
probabilistic approximation of the unknown function, depending on the
sequentially collected observations, while an acquisition function drives the
choice of the next solution to evaluate, balancing between exploration and
exploitation, depending on the current Gaussian Process model. Despite the huge
effort of the scientific community in defining effective
exploration-exploitation mechanisms, we are still far away from the master
acquisition function. This paper merges the most relevant results and insights
from both algorithmic and human search strategies to propose a novel
acquisition function, mastering the trade-off between explorative and
exploitative choices, adaptively. We compare the proposed acquisition function
on a number of test functions and against different state-of-the-art ones,
which are instead based on prefixed or random scheduling between exploration
and exploitation. A Pareto analysis is performed with respect to two
(antagonistic) goals: convergence to the optimum and exploration capability.
Results empirically prove that the proposed acquisition function is almost
always Pareto optimal and also the most balanced trade-off between the two
goals. | [
"cs.LG",
"cs.AI",
"math.OC"
] | false |
2305.08657 | 2023-05-15T14:02:35Z | Encoding Domain Expertise into Multilevel Models for Source Location | [
"Lawrence A. Bull",
"Matthew R. Jones",
"Elizabeth J. Cross",
"Andrew Duncan",
"Mark Girolami"
] | Data from populations of systems are prevalent in many industrial
applications. Machines and infrastructure are increasingly instrumented with
sensing systems, emitting streams of telemetry data with complex
interdependencies. In practice, data-centric monitoring procedures tend to
consider these assets (and respective models) as distinct -- operating in
isolation and associated with independent data. In contrast, this work captures
the statistical correlations and interdependencies between models of a group of
systems. Utilising a Bayesian multilevel approach, the value of data can be
extended, since the population can be considered as a whole, rather than
constituent parts. Most interestingly, domain expertise and knowledge of the
underlying physics can be encoded in the model at the system, subgroup, or
population level. We present an example of acoustic emission (time-of-arrival)
mapping for source location, to illustrate how multilevel models naturally lend
themselves to representing aggregate systems in engineering. In particular, we
focus on constraining the combined models with domain knowledge to enhance
transfer learning and enable further insights at the population level. | [
"stat.ML",
"cs.LG",
"stat.AP"
] | false |
2305.08687 | 2023-05-15T14:43:27Z | Accelerated Algorithms for Nonlinear Matrix Decomposition with the ReLU
function | [
"Giovanni Seraghiti",
"Atharva Awari",
"Arnaud Vandaele",
"Margherita Porcelli",
"Nicolas Gillis"
] | In this paper, we study the following nonlinear matrix decomposition (NMD)
problem: given a sparse nonnegative matrix $X$, find a low-rank matrix $\Theta$
such that $X \approx f(\Theta)$, where $f$ is an element-wise nonlinear
function. We focus on the case where $f(\cdot) = \max(0, \cdot)$, the rectified
unit (ReLU) non-linear activation. We refer to the corresponding problem as
ReLU-NMD. We first provide a brief overview of the existing approaches that
were developed to tackle ReLU-NMD. Then we introduce two new algorithms: (1)
aggressive accelerated NMD (A-NMD) which uses an adaptive Nesterov
extrapolation to accelerate an existing algorithm, and (2) three-block NMD
(3B-NMD) which parametrizes $\Theta = WH$ and leads to a significant reduction
in the computational cost. We also propose an effective initialization strategy
based on the nuclear norm as a proxy for the rank function. We illustrate the
effectiveness of the proposed algorithms (available on gitlab) on synthetic and
real-world data sets. | [
"cs.LG",
"eess.SP",
"math.OC",
"stat.ML"
] | false |
2305.08744 | 2023-05-15T15:55:12Z | Integrating Uncertainty into Neural Network-based Speech Enhancement | [
"Huajian Fang",
"Dennis Becker",
"Stefan Wermter",
"Timo Gerkmann"
] | Supervised masking approaches in the time-frequency domain aim to employ deep
neural networks to estimate a multiplicative mask to extract clean speech. This
leads to a single estimate for each input without any guarantees or measures of
reliability. In this paper, we study the benefits of modeling uncertainty in
clean speech estimation. Prediction uncertainty is typically categorized into
aleatoric uncertainty and epistemic uncertainty. The former refers to inherent
randomness in data, while the latter describes uncertainty in the model
parameters. In this work, we propose a framework to jointly model aleatoric and
epistemic uncertainties in neural network-based speech enhancement. The
proposed approach captures aleatoric uncertainty by estimating the statistical
moments of the speech posterior distribution and explicitly incorporates the
uncertainty estimate to further improve clean speech estimation. For epistemic
uncertainty, we investigate two Bayesian deep learning approaches: Monte Carlo
dropout and Deep ensembles to quantify the uncertainty of the neural network
parameters. Our analyses show that the proposed framework promotes capturing
practical and reliable uncertainty, while combining different sources of
uncertainties yields more reliable predictive uncertainty estimates.
Furthermore, we demonstrate the benefits of modeling uncertainty on speech
enhancement performance by evaluating the framework on different datasets,
exhibiting notable improvement over comparable models that fail to account for
uncertainty. | [
"eess.AS",
"cs.LG",
"cs.SD"
] | false |
2305.08770 | 2023-05-15T16:27:09Z | Transactional Python for Durable Machine Learning: Vision, Challenges,
and Feasibility | [
"Supawit Chockchowwat",
"Zhaoheng Li",
"Yongjoo Park"
] | In machine learning (ML), Python serves as a convenient abstraction for
working with key libraries such as PyTorch, scikit-learn, and others. Unlike
DBMS, however, Python applications may lose important data, such as trained
models and extracted features, due to machine failures or human errors, leading
to a waste of time and resources. Specifically, they lack four essential
properties that could make ML more reliable and user-friendly -- durability,
atomicity, replicability, and time-versioning (DART).
This paper presents our vision of Transactional Python that provides DART
without any code modifications to user programs or the Python kernel, by
non-intrusively monitoring application states at the object level and
determining a minimal amount of information sufficient to reconstruct a whole
application. Our evaluation of a proof-of-concept implementation with public
PyTorch and scikit-learn applications shows that DART can be offered with
overheads ranging 1.5%--15.6%. | [
"cs.DB",
"cs.LG",
"cs.PL"
] | false |
2305.08846 | 2023-05-15T17:57:56Z | Privacy Auditing with One (1) Training Run | [
"Thomas Steinke",
"Milad Nasr",
"Matthew Jagielski"
] | We propose a scheme for auditing differentially private machine learning
systems with a single training run. This exploits the parallelism of being able
to add or remove multiple training examples independently. We analyze this
using the connection between differential privacy and statistical
generalization, which avoids the cost of group privacy. Our auditing scheme
requires minimal assumptions about the algorithm and can be applied in the
black-box or white-box setting. | [
"cs.LG",
"cs.CR",
"cs.DS"
] | false |
2305.08849 | 2023-05-15T17:59:02Z | Learning on Manifolds: Universal Approximations Properties using
Geometric Controllability Conditions for Neural ODEs | [
"Karthik Elamvazhuthi",
"Xuechen Zhang",
"Samet Oymak",
"Fabio Pasqualetti"
] | In numerous robotics and mechanical engineering applications, among others,
data is often constrained on smooth manifolds due to the presence of rotational
degrees of freedom. Common datadriven and learning-based methods such as neural
ordinary differential equations (ODEs), however, typically fail to satisfy
these manifold constraints and perform poorly for these applications. To
address this shortcoming, in this paper we study a class of neural ordinary
differential equations that, by design, leave a given manifold invariant, and
characterize their properties by leveraging the controllability properties of
control affine systems. In particular, using a result due to Agrachev and
Caponigro on approximating diffeomorphisms with flows of feedback control
systems, we show that any map that can be represented as the flow of a
manifold-constrained dynamical system can also be approximated using the flow
of manifold-constrained neural ODE, whenever a certain controllability
condition is satisfied. Additionally, we show that this universal approximation
property holds when the neural ODE has limited width in each layer, thus
leveraging the depth of network instead for approximation. We verify our
theoretical findings using numerical experiments on PyTorch for the manifolds
S2 and the 3-dimensional orthogonal group SO(3), which are model manifolds for
mechanical systems such as spacecrafts and satellites. We also compare the
performance of the manifold invariant neural ODE with classical neural ODEs
that ignore the manifold invariant properties and show the superiority of our
approach in terms of accuracy and sample complexity. | [
"math.OC",
"cs.LG",
"cs.SY",
"eess.SY"
] | false |
2305.08929 | 2023-05-15T18:06:08Z | AF2-Mutation: Adversarial Sequence Mutations against AlphaFold2 on
Protein Tertiary Structure Prediction | [
"Zhongju Yuan",
"Tao Shen",
"Sheng Xu",
"Leiye Yu",
"Ruobing Ren",
"Siqi Sun"
] | Deep learning-based approaches, such as AlphaFold2 (AF2), have significantly
advanced protein tertiary structure prediction, achieving results comparable to
real biological experimental methods. While AF2 has shown limitations in
predicting the effects of mutations, its robustness against sequence mutations
remains to be determined. Starting with the wild-type (WT) sequence, we
investigate adversarial sequences generated via an evolutionary approach, which
AF2 predicts to be substantially different from WT. Our experiments on CASP14
reveal that by modifying merely three residues in the protein sequence using a
combination of replacement, deletion, and insertion strategies, the alteration
in AF2's predictions, as measured by the Local Distance Difference Test (lDDT),
reaches 46.61. Moreover, when applied to a specific protein, SPNS2, our
proposed algorithm successfully identifies biologically meaningful residues
critical to protein structure determination and potentially indicates
alternative conformations, thus significantly expediting the experimental
process. | [
"q-bio.BM",
"cs.AI",
"cs.LG"
] | false |
2305.08985 | 2023-05-15T19:55:51Z | Federated Learning over Harmonized Data Silos | [
"Dimitris Stripelis",
"Jose Luis Ambite"
] | Federated Learning is a distributed machine learning approach that enables
geographically distributed data silos to collaboratively learn a joint machine
learning model without sharing data. Most of the existing work operates on
unstructured data, such as images or text, or on structured data assumed to be
consistent across the different sites. However, sites often have different
schemata, data formats, data values, and access patterns. The field of data
integration has developed many methods to address these challenges, including
techniques for data exchange and query rewriting using declarative schema
mappings, and for entity linkage. Therefore, we propose an architectural vision
for an end-to-end Federated Learning and Integration system, incorporating the
critical steps of data harmonization and data imputation, to spur further
research on the intersection of data management information systems and machine
learning. | [
"cs.LG",
"cs.AI",
"cs.DC",
"68T07, 68M14,",
"I.2; H.4"
] | false |
2305.09006 | 2023-05-15T20:41:39Z | Physics-enhanced Gaussian Process Variational Autoencoder | [
"Thomas Beckers",
"Qirui Wu",
"George J. Pappas"
] | Variational autoencoders allow to learn a lower-dimensional latent space
based on high-dimensional input/output data. Using video clips as input data,
the encoder may be used to describe the movement of an object in the video
without ground truth data (unsupervised learning). Even though the object's
dynamics is typically based on first principles, this prior knowledge is mostly
ignored in the existing literature. Thus, we propose a physics-enhanced
variational autoencoder that places a physical-enhanced Gaussian process prior
on the latent dynamics to improve the efficiency of the variational autoencoder
and to allow physically correct predictions. The physical prior knowledge
expressed as linear dynamical system is here reflected by the Green's function
and included in the kernel function of the Gaussian process. The benefits of
the proposed approach are highlighted in a simulation with an oscillating
particle. | [
"cs.LG",
"cs.SY",
"eess.SY"
] | false |
2305.09017 | 2023-05-15T20:59:41Z | Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with
Physics Prior | [
"Thomas Beckers",
"Jacob Seidman",
"Paris Perdikaris",
"George J. Pappas"
] | Data-driven approaches achieve remarkable results for the modeling of complex
dynamics based on collected data. However, these models often neglect basic
physical principles which determine the behavior of any real-world system. This
omission is unfavorable in two ways: The models are not as data-efficient as
they could be by incorporating physical prior knowledge, and the model itself
might not be physically correct. We propose Gaussian Process Port-Hamiltonian
systems (GP-PHS) as a physics-informed Bayesian learning approach with
uncertainty quantification. The Bayesian nature of GP-PHS uses collected data
to form a distribution over all possible Hamiltonians instead of a single point
estimate. Due to the underlying physics model, a GP-PHS generates passive
systems with respect to designated inputs and outputs. Further, the proposed
approach preserves the compositional nature of Port-Hamiltonian systems. | [
"eess.SY",
"cs.LG",
"cs.SY"
] | false |
2305.09058 | 2023-05-15T22:56:27Z | Private Training Set Inspection in MLaaS | [
"Mingxue Xu",
"Tongtong Xu",
"Po-Yu Chen"
] | Machine Learning as a Service (MLaaS) is a popular cloud-based solution for
customers who aim to use an ML model but lack training data, computation
resources, or expertise in ML. In this case, the training datasets are
typically a private possession of the ML or data companies and are inaccessible
to the customers, but the customers still need an approach to confirm that the
training datasets meet their expectations and fulfil regulatory measures like
fairness. However, no existing work addresses the above customers' concerns.
This work is the first attempt to solve this problem, taking data origin as an
entry point. We first define origin membership measurement and based on this,
we then define diversity and fairness metrics to address customers' concerns.
We then propose a strategy to estimate the values of these two metrics in the
inaccessible training dataset, combining shadow training techniques from
membership inference and an efficient featurization scheme in multiple instance
learning. The evaluation contains an application of text review polarity
classification applications based on the language BERT model. Experimental
results show that our solution can achieve up to 0.87 accuracy for membership
inspection and up to 99.3% confidence in inspecting diversity and fairness
distribution. | [
"cs.LG",
"cs.CY",
"cs.DB"
] | false |
2305.09064 | 2023-05-15T23:17:26Z | Capturing Humans' Mental Models of AI: An Item Response Theory Approach | [
"Markelle Kelly",
"Aakriti Kumar",
"Padhraic Smyth",
"Mark Steyvers"
] | Improving our understanding of how humans perceive AI teammates is an
important foundation for our general understanding of human-AI teams. Extending
relevant work from cognitive science, we propose a framework based on item
response theory for modeling these perceptions. We apply this framework to
real-world experiments, in which each participant works alongside another
person or an AI agent in a question-answering setting, repeatedly assessing
their teammate's performance. Using this experimental data, we demonstrate the
use of our framework for testing research questions about people's perceptions
of both AI agents and other people. We contrast mental models of AI teammates
with those of human teammates as we characterize the dimensionality of these
mental models, their development over time, and the influence of the
participants' own self-perception. Our results indicate that people expect AI
agents' performance to be significantly better on average than the performance
of other humans, with less variation across different types of problems. We
conclude with a discussion of the implications of these findings for human-AI
interaction. | [
"cs.LG",
"cs.AI",
"cs.HC"
] | false |
2305.09686 | 2023-05-15T10:07:27Z | Data Bias Management | [
"Gianluca Demartini",
"Kevin Roitero",
"Stefano Mizzaro"
] | Due to the widespread use of data-powered systems in our everyday lives,
concepts like bias and fairness gained significant attention among researchers
and practitioners, in both industry and academia. Such issues typically emerge
from the data, which comes with varying levels of quality, used to train
supervised machine learning systems. With the commercialization and deployment
of such systems that are sometimes delegated to make life-changing decisions,
significant efforts are being made towards the identification and removal of
possible sources of data bias that may resurface to the final end user or in
the decisions being made. In this paper, we present research results that show
how bias in data affects end users, where bias is originated, and provide a
viewpoint about what we should do about it. We argue that data bias is not
something that should necessarily be removed in all cases, and that research
attention should instead shift from bias removal towards the identification,
measurement, indexing, surfacing, and adapting for bias, which we name bias
management. | [
"cs.LG",
"cs.AI",
"cs.IR"
] | false |
2305.09690 | 2023-05-15T22:20:07Z | A Whisper transformer for audio captioning trained with synthetic
captions and transfer learning | [
"Marek Kadlčík",
"Adam Hájek",
"Jürgen Kieslich",
"Radosław Winiecki"
] | The field of audio captioning has seen significant advancements in recent
years, driven by the availability of large-scale audio datasets and
advancements in deep learning techniques. In this technical report, we present
our approach to audio captioning, focusing on the use of a pretrained
speech-to-text Whisper model and pretraining on synthetic captions. We discuss
our training procedures and present our experiments' results, which include
model size variations, dataset mixtures, and other hyperparameters. Our
findings demonstrate the impact of different training strategies on the
performance of the audio captioning model. Our code and trained models are
publicly available on GitHub and Hugging Face Hub. | [
"cs.SD",
"cs.LG",
"eess.AS"
] | false |
2305.09691 | 2023-05-15T23:55:49Z | Evaluation Strategy of Time-series Anomaly Detection with Decay Function | [
"Yongwan Gim",
"Kyushik Min"
] | Recent algorithms of time-series anomaly detection have been evaluated by
applying a Point Adjustment (PA) protocol. However, the PA protocol has a
problem of overestimating the performance of the detection algorithms because
it only depends on the number of detected abnormal segments and their size. We
propose a novel evaluation protocol called the Point-Adjusted protocol with
decay function (PAdf) to evaluate the time-series anomaly detection algorithm
by reflecting the following ideal requirements: detect anomalies quickly and
accurately without false alarms. This paper theoretically and experimentally
shows that the PAdf protocol solves the over- and under-estimation problems of
existing protocols such as PA and PA\%K. By conducting re-evaluations of SOTA
models in benchmark datasets, we show that the PA protocol only focuses on
finding many anomalous segments, whereas the score of the PAdf protocol
considers not only finding many segments but also detecting anomalies quickly
without delay. | [
"cs.LG",
"cs.AI",
"stat.ME"
] | false |
2305.09046 | 2023-05-15T22:14:22Z | Convex optimization over a probability simplex | [
"James Chok",
"Geoffrey M. Vasil"
] | We propose a new iteration scheme, the Cauchy-Simplex, to optimize convex
problems over the probability simplex $\{w\in\mathbb{R}^n\ |\ \sum_i w_i=1\
\textrm{and}\ w_i\geq0\}$. Other works have taken steps to enforce positivity
or unit normalization automatically but never simultaneously within a unified
setting. This paper presents a natural framework for manifestly requiring the
probability condition. Specifically, we map the simplex to the positive
quadrant of a unit sphere, envisage gradient descent in latent variables, and
map the result back in a way that only depends on the simplex variable.
Moreover, proving rigorous convergence results in this formulation leads
inherently to tools from information theory (e.g. cross entropy and KL
divergence). Each iteration of the Cauchy-Simplex consists of simple
operations, making it well-suited for high-dimensional problems. We prove that
it has a convergence rate of ${O}(1/T)$ for convex functions, and numerical
experiments of projection onto convex hulls show faster convergence than
similar algorithms. Finally, we apply our algorithm to online learning problems
and prove the convergence of the average regret for (1) Prediction with expert
advice and (2) Universal Portfolios. | [
"math.OC",
"cs.LG",
"cs.NA",
"math.NA",
"q-fin.PM",
"stat.ML",
"65K10, 68W27, 68W40, 91G10, 97U40"
] | false |
2305.09078 | 2023-05-16T00:37:58Z | PanelNet: Understanding 360 Indoor Environment via Panel Representation | [
"Haozheng Yu",
"Lu He",
"Bing Jian",
"Weiwei Feng",
"Shan Liu"
] | Indoor 360 panoramas have two essential properties. (1) The panoramas are
continuous and seamless in the horizontal direction. (2) Gravity plays an
important role in indoor environment design. By leveraging these properties, we
present PanelNet, a framework that understands indoor environments using a
novel panel representation of 360 images. We represent an equirectangular
projection (ERP) as consecutive vertical panels with corresponding 3D panel
geometry. To reduce the negative impact of panoramic distortion, we incorporate
a panel geometry embedding network that encodes both the local and global
geometric features of a panel. To capture the geometric context in room design,
we introduce Local2Global Transformer, which aggregates local information
within a panel and panel-wise global context. It greatly improves the model
performance with low training overhead. Our method outperforms existing methods
on indoor 360 depth estimation and shows competitive results against
state-of-the-art approaches on the task of indoor layout estimation and
semantic segmentation. | [
"cs.CV"
] | false |
2305.09095 | 2023-05-16T01:41:10Z | Multi-view MERA Subspace Clustering | [
"Zhen Long",
"Ce Zhu",
"Jie Chen",
"Zihan Li",
"Yazhou Ren",
"Yipeng Liu"
] | Tensor-based multi-view subspace clustering (MSC) can capture high-order
correlation in the self-representation tensor. Current tensor decompositions
for MSC suffer from highly unbalanced unfolding matrices or rotation
sensitivity, failing to fully explore inter/intra-view information. Using the
advanced tensor network, namely, multi-scale entanglement renormalization
ansatz (MERA), we propose a low-rank MERA based MSC (MERA-MSC) algorithm, where
MERA factorizes a tensor into contractions of one top core factor and the rest
orthogonal/semi-orthogonal factors. Benefiting from multiple interactions among
orthogonal/semi-orthogonal (low-rank) factors, the low-rank MERA has a strong
representation power to capture the complex inter/intra-view information in the
self-representation tensor. The alternating direction method of multipliers is
adopted to solve the optimization model. Experimental results on five
multi-view datasets demonstrate MERA-MSC has superiority against the compared
algorithms on six evaluation metrics. Furthermore, we extend MERA-MSC by
incorporating anchor learning to develop a scalable low-rank MERA based
multi-view clustering method (sMREA-MVC). The effectiveness and efficiency of
sMERA-MVC have been validated on three large-scale multi-view datasets. To our
knowledge, this is the first work to introduce MERA to the multi-view
clustering topic. The codes of MERA-MSC and sMERA-MVC are publicly available at
https://github.com/longzhen520/MERA-MSC. | [
"cs.CV"
] | false |
2305.09183 | 2023-05-16T05:46:31Z | Lightweight Self-Knowledge Distillation with Multi-source Information
Fusion | [
"Xucong Wang",
"Pengchao Han",
"Lei Guo"
] | Knowledge Distillation (KD) is a powerful technique for transferring
knowledge between neural network models, where a pre-trained teacher model is
used to facilitate the training of the target student model. However, the
availability of a suitable teacher model is not always guaranteed. To address
this challenge, Self-Knowledge Distillation (SKD) attempts to construct a
teacher model from itself. Existing SKD methods add Auxiliary Classifiers (AC)
to intermediate layers of the model or use the history models and models with
different input data within the same class. However, these methods are
computationally expensive and only capture time-wise and class-wise features of
data. In this paper, we propose a lightweight SKD framework that utilizes
multi-source information to construct a more informative teacher. Specifically,
we introduce a Distillation with Reverse Guidance (DRG) method that considers
different levels of information extracted by the model, including edge, shape,
and detail of the input data, to construct a more informative teacher.
Additionally, we design a Distillation with Shape-wise Regularization (DSR)
method that ensures a consistent shape of ranked model output for all data. We
validate the performance of the proposed DRG, DSR, and their combination
through comprehensive experiments on various datasets and models. Our results
demonstrate the superiority of the proposed methods over baselines (up to
2.87%) and state-of-the-art SKD methods (up to 1.15%), while being
computationally efficient and robust. The code is available at
https://github.com/xucong-parsifal/LightSKD. | [
"cs.CV"
] | false |
2305.09195 | 2023-05-16T06:07:20Z | Correlation Pyramid Network for 3D Single Object Tracking | [
"Mengmeng Wang",
"Teli Ma",
"Xingxing Zuo",
"Jiajun Lv",
"Yong Liu"
] | 3D LiDAR-based single object tracking (SOT) has gained increasing attention
as it plays a crucial role in 3D applications such as autonomous driving. The
central problem is how to learn a target-aware representation from the sparse
and incomplete point clouds. In this paper, we propose a novel Correlation
Pyramid Network (CorpNet) with a unified encoder and a motion-factorized
decoder. Specifically, the encoder introduces multi-level self attentions and
cross attentions in its main branch to enrich the template and search region
features and realize their fusion and interaction, respectively. Additionally,
considering the sparsity characteristics of the point clouds, we design a
lateral correlation pyramid structure for the encoder to keep as many points as
possible by integrating hierarchical correlated features. The output features
of the search region from the encoder can be directly fed into the decoder for
predicting target locations without any extra matcher. Moreover, in the decoder
of CorpNet, we design a motion-factorized head to explicitly learn the
different movement patterns of the up axis and the x-y plane together.
Extensive experiments on two commonly-used datasets show our CorpNet achieves
state-of-the-art results while running in real-time. | [
"cs.CV"
] | false |
2305.09271 | 2023-05-16T08:25:27Z | Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement | [
"Arian Bakhtiarnia",
"Qi Zhang",
"Alexandros Iosifidis"
] | The increasing prevalence of gigapixel resolutions has presented new
challenges for crowd counting. Such resolutions are far beyond the memory and
computation limits of current GPUs, and available deep neural network
architectures and training procedures are not designed for such massive inputs.
Although several methods have been proposed to address these challenges, they
are either limited to downsampling the input image to a small size, or
borrowing from other gigapixel tasks, which are not tailored for crowd
counting. In this paper, we propose a novel method called GigaZoom, which
iteratively zooms into the densest areas of the image and refines coarser
density maps with finer details. Through experiments, we show that GigaZoom
obtains the state-of-the-art for gigapixel crowd counting and improves the
accuracy of the next best method by 42%. | [
"cs.CV"
] | false |
2305.09285 | 2023-05-16T08:43:14Z | Latent Distribution Adjusting for Face Anti-Spoofing | [
"Qinghong Sun",
"Zhenfei Yin",
"Yichao Wu",
"Yuanhan Zhang",
"Jing Shao"
] | With the development of deep learning, the field of face anti-spoofing (FAS)
has witnessed great progress. FAS is usually considered a classification
problem, where each class is assumed to contain a single cluster optimized by
softmax loss. In practical deployment, one class can contain several local
clusters, and a single-center is insufficient to capture the inherent structure
of the FAS data. However, few approaches consider large distribution
discrepancies in the field of FAS. In this work, we propose a unified framework
called Latent Distribution Adjusting (LDA) with properties of latent,
discriminative, adaptive, generic to improve the robustness of the FAS model by
adjusting complex data distribution with multiple prototypes. 1) Latent. LDA
attempts to model the data of each class as a Gaussian mixture distribution,
and acquire a flexible number of centers for each class in the last fully
connected layer implicitly. 2) Discriminative. To enhance the intra-class
compactness and inter-class discrepancy, we propose a margin-based loss for
providing distribution constrains for prototype learning. 3) Adaptive. To make
LDA more efficient and decrease redundant parameters, we propose Adaptive
Prototype Selection (APS) by selecting the appropriate number of centers
adaptively according to different distributions. 4) Generic. Furthermore, LDA
can adapt to unseen distribution by utilizing very few training data without
re-training. Extensive experiments demonstrate that our framework can 1) make
the final representation space both intra-class compact and inter-class
separable, 2) outperform the state-of-the-art methods on multiple standard FAS
benchmarks. | [
"cs.CV"
] | false |
2305.09407 | 2023-05-16T12:51:51Z | A Novel Strategy for Improving Robustness in Computer Vision
Manufacturing Defect Detection | [
"Ahmad Mohamad Mezher",
"Andrew E. Marble"
] | Visual quality inspection in high performance manufacturing can benefit from
automation, due to cost savings and improved rigor. Deep learning techniques
are the current state of the art for generic computer vision tasks like
classification and object detection. Manufacturing data can pose a challenge
for deep learning because data is highly repetitive and there are few images of
defects or deviations to learn from. Deep learning models trained with such
data can be fragile and sensitive to context, and can under-detect new defects
not found in the training data. In this work, we explore training defect
detection models to learn specific defects out of context, so that they are
more likely to be detected in new situations. We demonstrate how models trained
on diverse images containing a common defect type can pick defects out in new
circumstances. Such generic models could be more robust to new defects not
found data collected for training, and can reduce data collection impediments
to implementing visual inspection on production lines. Additionally, we
demonstrate that object detection models trained to predict a label and
bounding box outperform classifiers that predict a label only on held out test
data typical of manufacturing inspection tasks. Finally, we studied the factors
that affect generalization in order to train models that work under a wider
range of conditions. | [
"cs.CV"
] | false |
2305.09523 | 2023-05-16T15:18:42Z | SCTracker: Multi-object tracking with shape and confidence constraints | [
"Huan Mao",
"Yulin Chen",
"Zongtan Li",
"Feng Chen",
"Pingping Chen"
] | Detection-based tracking is one of the main methods of multi-object tracking.
It can obtain good tracking results when using excellent detectors but it may
associate wrong targets when facing overlapping and low-confidence detections.
To address this issue, this paper proposes a multi-object tracker based on
shape constraint and confidence named SCTracker. In the data association stage,
an Intersection of Union distance with shape constraints is applied to
calculate the cost matrix between tracks and detections, which can effectively
avoid the track tracking to the wrong target with the similar position but
inconsistent shape, so as to improve the accuracy of data association.
Additionally, the Kalman Filter based on the detection confidence is used to
update the motion state to improve the tracking performance when the detection
has low confidence. Experimental results on MOT 17 dataset show that the
proposed method can effectively improve the tracking performance of
multi-object tracking. | [
"cs.CV"
] | false |
2305.09539 | 2023-05-16T15:30:33Z | Learning Higher-order Object Interactions for Keypoint-based Video
Understanding | [
"Yi Huang",
"Asim Kadav",
"Farley Lai",
"Deep Patel",
"Hans Peter Graf"
] | Action recognition is an important problem that requires identifying actions
in video by learning complex interactions across scene actors and objects.
However, modern deep-learning based networks often require significant
computation, and may capture scene context using various modalities that
further increases compute costs. Efficient methods such as those used for AR/VR
often only use human-keypoint information but suffer from a loss of scene
context that hurts accuracy. In this paper, we describe an action-localization
method, KeyNet, that uses only the keypoint data for tracking and action
recognition. Specifically, KeyNet introduces the use of object based keypoint
information to capture context in the scene. Our method illustrates how to
build a structured intermediate representation that allows modeling
higher-order interactions in the scene from object and human keypoints without
using any RGB information. We find that KeyNet is able to track and classify
human actions at just 5 FPS. More importantly, we demonstrate that object
keypoints can be modeled to recover any loss in context from using keypoint
information over AVA action and Kinetics datasets. | [
"cs.CV"
] | false |
2305.09542 | 2023-05-16T15:34:12Z | Increasing Melanoma Diagnostic Confidence: Forcing the Convolutional
Network to Learn from the Lesion | [
"Norsang Lama",
"R. Joe Stanley",
"Anand Nambisan",
"Akanksha Maurya",
"Jason Hagerty",
"William V. Stoecker"
] | Deep learning implemented with convolutional network architectures can exceed
specialists' diagnostic accuracy. However, whole-image deep learning trained on
a given dataset may not generalize to other datasets. The problem arises
because extra-lesional features - ruler marks, ink marks, and other melanoma
correlates - may serve as information leaks. These extra-lesional features,
discoverable by heat maps, degrade melanoma diagnostic performance and cause
techniques learned on one data set to fail to generalize. We propose a novel
technique to improve melanoma recognition by an EfficientNet model. The model
trains the network to detect the lesion and learn features from the detected
lesion. A generalizable elliptical segmentation model for lesions was
developed, with an ellipse enclosing a lesion and the ellipse enclosed by an
extended rectangle (bounding box). The minimal bounding box was extended by 20%
to allow some background around the lesion. The publicly available
International Skin Imaging Collaboration (ISIC) 2020 skin lesion image dataset
was used to evaluate the effectiveness of the proposed method. Our test results
show that the proposed method improved diagnostic accuracy by increasing the
mean area under receiver operating characteristic curve (mean AUC) score from
0.9 to 0.922. Additionally, correctly diagnosed scores are also improved,
providing better separation of scores, thereby increasing melanoma diagnostic
confidence. The proposed lesion-focused convolutional technique warrants
further study. | [
"cs.CV"
] | false |
2305.09602 | 2023-05-16T16:54:48Z | Urban-StyleGAN: Learning to Generate and Manipulate Images of Urban
Scenes | [
"George Eskandar",
"Youssef Farag",
"Tarun Yenamandra",
"Daniel Cremers",
"Karim Guirguis",
"Bin Yang"
] | A promise of Generative Adversarial Networks (GANs) is to provide cheap
photorealistic data for training and validating AI models in autonomous
driving. Despite their huge success, their performance on complex images
featuring multiple objects is understudied. While some frameworks produce
high-quality street scenes with little to no control over the image content,
others offer more control at the expense of high-quality generation. A common
limitation of both approaches is the use of global latent codes for the whole
image, which hinders the learning of independent object distributions.
Motivated by SemanticStyleGAN (SSG), a recent work on latent space
disentanglement in human face generation, we propose a novel framework,
Urban-StyleGAN, for urban scene generation and manipulation. We find that a
straightforward application of SSG leads to poor results because urban scenes
are more complex than human faces. To provide a more compact yet disentangled
latent representation, we develop a class grouping strategy wherein individual
classes are grouped into super-classes. Moreover, we employ an unsupervised
latent exploration algorithm in the $\mathcal{S}$-space of the generator and
show that it is more efficient than the conventional $\mathcal{W}^{+}$-space in
controlling the image content. Results on the Cityscapes and Mapillary datasets
show the proposed approach achieves significantly more controllability and
improved image quality than previous approaches on urban scenes and is on par
with general-purpose non-controllable generative models (like StyleGAN2) in
terms of quality. | [
"cs.CV"
] | false |
2305.09699 | 2023-05-16T07:16:36Z | Mobile User Interface Element Detection Via Adaptively Prompt Tuning | [
"Zhangxuan Gu",
"Zhuoer Xu",
"Haoxing Chen",
"Jun Lan",
"Changhua Meng",
"Weiqiang Wang"
] | Recent object detection approaches rely on pretrained vision-language models
for image-text alignment. However, they fail to detect the Mobile User
Interface (MUI) element since it contains additional OCR information, which
describes its content and function but is often ignored. In this paper, we
develop a new MUI element detection dataset named MUI-zh and propose an
Adaptively Prompt Tuning (APT) module to take advantage of discriminating OCR
information. APT is a lightweight and effective module to jointly optimize
category prompts across different modalities. For every element, APT uniformly
encodes its visual features and OCR descriptions to dynamically adjust the
representation of frozen category prompts. We evaluate the effectiveness of our
plug-and-play APT upon several existing CLIP-based detectors for both standard
and open-vocabulary MUI element detection. Extensive experiments show that our
method achieves considerable improvements on two datasets. The datasets is
available at \url{github.com/antmachineintelligence/MUI-zh}. | [
"cs.CV"
] | false |
2305.09726 | 2023-05-16T18:01:12Z | Towards Pragmatic Semantic Image Synthesis for Urban Scenes | [
"George Eskandar",
"Diandian Guo",
"Karim Guirguis",
"Bin Yang"
] | The need for large amounts of training and validation data is a huge concern
in scaling AI algorithms for autonomous driving. Semantic Image Synthesis
(SIS), or label-to-image translation, promises to address this issue by
translating semantic layouts to images, providing a controllable generation of
photorealistic data. However, they require a large amount of paired data,
incurring extra costs. In this work, we present a new task: given a dataset
with synthetic images and labels and a dataset with unlabeled real images, our
goal is to learn a model that can generate images with the content of the input
mask and the appearance of real images. This new task reframes the well-known
unsupervised SIS task in a more practical setting, where we leverage cheaply
available synthetic data from a driving simulator to learn how to generate
photorealistic images of urban scenes. This stands in contrast to previous
works, which assume that labels and images come from the same domain but are
unpaired during training. We find that previous unsupervised works underperform
on this task, as they do not handle distribution shifts between two different
domains. To bypass these problems, we propose a novel framework with two main
contributions. First, we leverage the synthetic image as a guide to the content
of the generated image by penalizing the difference between their high-level
features on a patch level. Second, in contrast to previous works which employ
one discriminator that overfits the target domain semantic distribution, we
employ a discriminator for the whole image and multiscale discriminators on the
image patches. Extensive comparisons on the benchmarks GTA-V $\rightarrow$
Cityscapes and GTA-V $\rightarrow$ Mapillary show the superior performance of
the proposed model against state-of-the-art on this task. | [
"cs.CV"
] | false |
2305.09750 | 2023-05-16T18:56:12Z | ICDAR 2023 Competition on Hierarchical Text Detection and Recognition | [
"Shangbang Long",
"Siyang Qin",
"Dmitry Panteleev",
"Alessandro Bissacco",
"Yasuhisa Fujii",
"Michalis Raptis"
] | We organize a competition on hierarchical text detection and recognition. The
competition is aimed to promote research into deep learning models and systems
that can jointly perform text detection and recognition and geometric layout
analysis. We present details of the proposed competition organization,
including tasks, datasets, evaluations, and schedule. During the competition
period (from January 2nd 2023 to April 1st 2023), at least 50 submissions from
more than 20 teams were made in the 2 proposed tasks. Considering the number of
teams and submissions, we conclude that the HierText competition has been
successfully held. In this report, we will also present the competition results
and insights from them. | [
"cs.CV"
] | false |
2305.09810 | 2023-05-16T21:24:26Z | Semi-Supervised Object Detection for Sorghum Panicles in UAV Imagery | [
"Enyu Cai",
"Jiaqi Guo",
"Changye Yang",
"Edward J. Delp"
] | The sorghum panicle is an important trait related to grain yield and plant
development. Detecting and counting sorghum panicles can provide significant
information for plant phenotyping. Current deep-learning-based object detection
methods for panicles require a large amount of training data. The data labeling
is time-consuming and not feasible for real application. In this paper, we
present an approach to reduce the amount of training data for sorghum panicle
detection via semi-supervised learning. Results show we can achieve similar
performance as supervised methods for sorghum panicle detection by only using
10\% of original training data. | [
"cs.CV"
] | false |
2305.09092 | 2023-05-16T01:29:26Z | ProtoVAE: Prototypical Networks for Unsupervised Disentanglement | [
"Vaishnavi Patil",
"Matthew Evanusa",
"Joseph JaJa"
] | Generative modeling and self-supervised learning have in recent years made
great strides towards learning from data in a completely unsupervised way.
There is still however an open area of investigation into guiding a neural
network to encode the data into representations that are interpretable or
explainable. The problem of unsupervised disentanglement is of particular
importance as it proposes to discover the different latent factors of variation
or semantic concepts from the data alone, without labeled examples, and encode
them into structurally disjoint latent representations. Without additional
constraints or inductive biases placed in the network, a generative model may
learn the data distribution and encode the factors, but not necessarily in a
disentangled way. Here, we introduce a novel deep generative VAE-based model,
ProtoVAE, that leverages a deep metric learning Prototypical network trained
using self-supervision to impose these constraints. The prototypical network
constrains the mapping of the representation space to data space to ensure that
controlled changes in the representation space are mapped to changes in the
factors of variations in the data space. Our model is completely unsupervised
and requires no a priori knowledge of the dataset, including the number of
factors. We evaluate our proposed model on the benchmark dSprites, 3DShapes,
and MPI3D disentanglement datasets, showing state of the art results against
previous methods via qualitative traversals in the latent space, as well as
quantitative disentanglement metrics. We further qualitatively demonstrate the
effectiveness of our model on the real-world CelebA dataset. | [
"cs.LG",
"cs.CV"
] | false |
2305.09141 | 2023-05-16T03:45:02Z | Deep Ensembling for Perceptual Image Quality Assessment | [
"Nisar Ahmed",
"H. M. Shahzad Asif",
"Abdul Rauf Bhatti",
"Atif Khan"
] | Blind image quality assessment is a challenging task particularly due to the
unavailability of reference information. Training a deep neural network
requires a large amount of training data which is not readily available for
image quality. Transfer learning is usually opted to overcome this limitation
and different deep architectures are used for this purpose as they learn
features differently. After extensive experiments, we have designed a deep
architecture containing two CNN architectures as its sub-units. Moreover, a
self-collected image database BIQ2021 is proposed with 12,000 images having
natural distortions. The self-collected database is subjectively scored and is
used for model training and validation. It is demonstrated that synthetic
distortion databases cannot provide generalization beyond the distortion types
used in the database and they are not ideal candidates for general-purpose
image quality assessment. Moreover, a large-scale database of 18.75 million
images with synthetic distortions is used to pretrain the model and then
retrain it on benchmark databases for evaluation. Experiments are conducted on
six benchmark databases three of which are synthetic distortion databases
(LIVE, CSIQ and TID2013) and three are natural distortion databases (LIVE
Challenge Database, CID2013 and KonIQ-10 k). The proposed approach has provided
a Pearson correlation coefficient of 0.8992, 0.8472 and 0.9452 subsequently and
Spearman correlation coefficient of 0.8863, 0.8408 and 0.9421. Moreover, the
performance is demonstrated using perceptually weighted rank correlation to
indicate the perceptual superiority of the proposed approach. Multiple
experiments are conducted to validate the generalization performance of the
proposed model by training on different subsets of the databases and validating
on the test subset of BIQ2021 database. | [
"cs.CV",
"eess.IV"
] | false |
2305.09147 | 2023-05-16T03:53:23Z | Self-Aware Trajectory Prediction for Safe Autonomous Driving | [
"Wenbo Shao",
"Jun Li",
"Hong Wang"
] | Trajectory prediction is one of the key components of the autonomous driving
software stack. Accurate prediction for the future movement of surrounding
traffic participants is an important prerequisite for ensuring the driving
efficiency and safety of intelligent vehicles. Trajectory prediction algorithms
based on artificial intelligence have been widely studied and applied in recent
years and have achieved remarkable results. However, complex artificial
intelligence models are uncertain and difficult to explain, so they may face
unintended failures when applied in the real world. In this paper, a self-aware
trajectory prediction method is proposed. By introducing a self-awareness
module and a two-stage training process, the original trajectory prediction
module's performance is estimated online, to facilitate the system to deal with
the possible scenario of insufficient prediction function in time, and create
conditions for the realization of safe and reliable autonomous driving.
Comprehensive experiments and analysis are performed, and the proposed method
performed well in terms of self-awareness, memory footprint, and real-time
performance, showing that it may serve as a promising paradigm for safe
autonomous driving. | [
"cs.RO",
"cs.CV"
] | false |
2305.09186 | 2023-05-16T05:50:47Z | Abnormal Functional Brain Network Connectivity Associated with
Alzheimer's Disease | [
"Yongcheng Yao"
] | The study's objective is to explore the distinctions in the functional brain
network connectivity between Alzheimer's Disease (AD) patients and normal
controls using Functional Magnetic Resonance Imaging (fMRI). The study included
590 individuals, with 175 having AD dementia and 415 age-, gender-, and
handedness-matched normal controls. The connectivity of functional brain
networks was measured using ROI-to-ROI and ROI-to-Voxel connectivity analyses.
The findings reveal a general decrease in functional connectivity among the AD
group in comparison to the normal control group. These results advance our
comprehension of AD pathophysiology and could assist in identifying AD
biomarkers. | [
"q-bio.NC",
"cs.CV"
] | false |
2305.09214 | 2023-05-16T06:44:17Z | PIQI: Perceptual Image Quality Index based on Ensemble of Gaussian
Process Regression | [
"Nisar Ahmed",
"Hafiz Muhammad Shahzad Asif",
"Hassan Khalid"
] | Digital images contain a lot of redundancies, therefore, compression
techniques are applied to reduce the image size without loss of reasonable
image quality. Same become more prominent in the case of videos which contains
image sequences and higher compression ratios are achieved in low throughput
networks. Assessment of quality of images in such scenarios has become of
particular interest. Subjective evaluation in most of the scenarios is
infeasible so objective evaluation is preferred. Among the three objective
quality measures, full-reference and reduced-reference methods require an
original image in some form to calculate the image quality which is unfeasible
in scenarios such as broadcasting, acquisition or enhancement. Therefore, a
no-reference Perceptual Image Quality Index (PIQI) is proposed in this paper to
assess the quality of digital images which calculates luminance and gradient
statistics along with mean subtracted contrast normalized products in multiple
scales and color spaces. These extracted features are provided to a stacked
ensemble of Gaussian Process Regression (GPR) to perform the perceptual quality
evaluation. The performance of the PIQI is checked on six benchmark databases
and compared with twelve state-of-the-art methods and competitive results are
achieved. The comparison is made based on RMSE, Pearson and Spearman
correlation coefficients between ground truth and predicted quality scores. The
scores of 0.0552, 0.9802 and 0.9776 are achieved respectively for these metrics
on CSIQ database. Two cross-dataset evaluation experiments are performed to
check the generalization of PIQI. | [
"cs.CV",
"eess.IV"
] | false |
2305.09236 | 2023-05-16T07:34:03Z | One-shot neural band selection for spectral recovery | [
"Hai-Miao Hu",
"Zhenbo Xu",
"Wenshuai Xu",
"You Song",
"YiTao Zhang",
"Liu Liu",
"Zhilin Han",
"Ajin Meng"
] | Band selection has a great impact on the spectral recovery quality. To solve
this ill-posed inverse problem, most band selection methods adopt hand-crafted
priors or exploit clustering or sparse regularization constraints to find most
prominent bands. These methods are either very slow due to the computational
cost of repeatedly training with respect to different selection frequencies or
different band combinations. Many traditional methods rely on the scene prior
and thus are not applicable to other scenarios. In this paper, we present a
novel one-shot Neural Band Selection (NBS) framework for spectral recovery.
Unlike conventional searching approaches with a discrete search space and a
non-differentiable search strategy, our NBS is based on the continuous
relaxation of the band selection process, thus allowing efficient band search
using gradient descent. To enable the compatibility for se- lecting any number
of bands in one-shot, we further exploit the band-wise correlation matrices to
progressively suppress similar adjacent bands. Extensive evaluations on the
NTIRE 2022 Spectral Reconstruction Challenge demonstrate that our NBS achieves
consistent performance gains over competitive baselines when examined with four
different spectral recov- ery methods. Our code will be publicly available. | [
"cs.CV",
"eess.IV"
] | false |
2305.09293 | 2023-05-16T09:01:42Z | Out-of-Distribution Detection for Adaptive Computer Vision | [
"Simon Kristoffersson Lind",
"Rudolph Triebel",
"Luigi Nardi",
"Volker Krueger"
] | It is well known that computer vision can be unreliable when faced with
previously unseen imaging conditions. This paper proposes a method to adapt
camera parameters according to a normalizing flow-based out-of-distibution
detector. A small-scale study is conducted which shows that adapting camera
parameters according to this out-of-distibution detector leads to an average
increase of 3 to 4 percentage points in mAP, mAR and F1 performance metrics of
a YOLOv4 object detector. As a secondary result, this paper also shows that it
is possible to train a normalizing flow model for out-of-distribution detection
on the COCO dataset, which is larger and more diverse than most benchmarks for
out-of-distibution detectors. | [
"cs.CV",
"cs.LG"
] | false |
2305.09299 | 2023-05-16T09:18:38Z | UniS-MMC: Multimodal Classification via Unimodality-supervised
Multimodal Contrastive Learning | [
"Heqing Zou",
"Meng Shen",
"Chen Chen",
"Yuchen Hu",
"Deepu Rajan",
"Eng Siong Chng"
] | Multimodal learning aims to imitate human beings to acquire complementary
information from multiple modalities for various downstream tasks. However,
traditional aggregation-based multimodal fusion methods ignore the
inter-modality relationship, treat each modality equally, suffer sensor noise,
and thus reduce multimodal learning performance. In this work, we propose a
novel multimodal contrastive method to explore more reliable multimodal
representations under the weak supervision of unimodal predicting.
Specifically, we first capture task-related unimodal representations and the
unimodal predictions from the introduced unimodal predicting task. Then the
unimodal representations are aligned with the more effective one by the
designed multimodal contrastive method under the supervision of the unimodal
predictions. Experimental results with fused features on two image-text
classification benchmarks UPMC-Food-101 and N24News show that our proposed
Unimodality-Supervised MultiModal Contrastive UniS-MMC learning method
outperforms current state-of-the-art multimodal methods. The detailed ablation
study and analysis further demonstrate the advantage of our proposed method. | [
"cs.CV",
"cs.CL"
] | false |
2305.09353 | 2023-05-16T11:17:54Z | Blind Image Quality Assessment via Transformer Predicted Error Map and
Perceptual Quality Token | [
"Jinsong Shi",
"Pan Gao",
"Aljosa Smolic"
] | Image quality assessment is a fundamental problem in the field of image
processing, and due to the lack of reference images in most practical
scenarios, no-reference image quality assessment (NR-IQA), has gained
increasing attention recently. With the development of deep learning
technology, many deep neural network-based NR-IQA methods have been developed,
which try to learn the image quality based on the understanding of database
information. Currently, Transformer has achieved remarkable progress in various
vision tasks. Since the characteristics of the attention mechanism in
Transformer fit the global perceptual impact of artifacts perceived by a human,
Transformer is thus well suited for image quality assessment tasks. In this
paper, we propose a Transformer based NR-IQA model using a predicted objective
error map and perceptual quality token. Specifically, we firstly generate the
predicted error map by pre-training one model consisting of a Transformer
encoder and decoder, in which the objective difference between the distorted
and the reference images is used as supervision. Then, we freeze the parameters
of the pre-trained model and design another branch using the vision Transformer
to extract the perceptual quality token for feature fusion with the predicted
error map. Finally, the fused features are regressed to the final image quality
score. Extensive experiments have shown that our proposed method outperforms
the current state-of-the-art in both authentic and synthetic image databases.
Moreover, the attentional map extracted by the perceptual quality token also
does conform to the characteristics of the human visual system. | [
"cs.CV",
"eess.IV"
] | false |
2305.09401 | 2023-05-16T12:33:51Z | Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for
Pedestrian Detection | [
"Andrew Farley",
"Mohsen Zand",
"Michael Greenspan"
] | We propose a method that augments a simulated dataset using diffusion models
to improve the performance of pedestrian detection in real-world data. The high
cost of collecting and annotating data in the real-world has motivated the use
of simulation platforms to create training datasets. While simulated data is
inexpensive to collect and annotate, it unfortunately does not always closely
match the distribution of real-world data, which is known as the sim2real gap.
In this paper we propose a novel method of synthetic data creation meant to
close the sim2real gap for the challenging pedestrian detection task. Our
method uses a diffusion-based architecture to learn a real-world distribution
which, once trained, is used to generate datasets. We mix this generated data
with simulated data as a form of augmentation and show that training on a
combination of generated and simulated data increases average precision by as
much as 27.3% for pedestrian detection models in real-world data, compared
against training on purely simulated data. | [
"cs.CV",
"cs.AI"
] | false |
2305.09504 | 2023-05-16T14:58:30Z | Content-Adaptive Downsampling in Convolutional Neural Networks | [
"Robin Hesse",
"Simone Schaub-Meyer",
"Stefan Roth"
] | Many convolutional neural networks (CNNs) rely on progressive downsampling of
their feature maps to increase the network's receptive field and decrease
computational cost. However, this comes at the price of losing granularity in
the feature maps, limiting the ability to correctly understand images or
recover fine detail in dense prediction tasks. To address this, common practice
is to replace the last few downsampling operations in a CNN with dilated
convolutions, allowing to retain the feature map resolution without reducing
the receptive field, albeit increasing the computational cost. This allows to
trade off predictive performance against cost, depending on the output feature
resolution. By either regularly downsampling or not downsampling the entire
feature map, existing work implicitly treats all regions of the input image and
subsequent feature maps as equally important, which generally does not hold. We
propose an adaptive downsampling scheme that generalizes the above idea by
allowing to process informative regions at a higher resolution than less
informative ones. In a variety of experiments, we demonstrate the versatility
of our adaptive downsampling strategy and empirically show that it improves the
cost-accuracy trade-off of various established CNNs. | [
"cs.CV",
"cs.LG"
] | false |
2305.09585 | 2023-05-16T16:32:08Z | Inductive Graph Neural Networks for Moving Object Segmentation | [
"Wieke Prummel",
"Jhony H. Giraldo",
"Anastasia Zakharova",
"Thierry Bouwmans"
] | Moving Object Segmentation (MOS) is a challenging problem in computer vision,
particularly in scenarios with dynamic backgrounds, abrupt lighting changes,
shadows, camouflage, and moving cameras. While graph-based methods have shown
promising results in MOS, they have mainly relied on transductive learning
which assumes access to the entire training and testing data for evaluation.
However, this assumption is not realistic in real-world applications where the
system needs to handle new data during deployment. In this paper, we propose a
novel Graph Inductive Moving Object Segmentation (GraphIMOS) algorithm based on
a Graph Neural Network (GNN) architecture. Our approach builds a generic model
capable of performing prediction on newly added data frames using the already
trained model. GraphIMOS outperforms previous inductive learning methods and is
more generic than previous transductive techniques. Our proposed algorithm
enables the deployment of graph-based MOS models in real-world applications. | [
"cs.CV",
"cs.LG"
] | false |
2305.09646 | 2023-05-16T17:45:32Z | torchosr -- a PyTorch extension package for Open Set Recognition models
evaluation in Python | [
"Joanna Komorniczak",
"Pawel Ksieniewicz"
] | The article presents the torchosr package - a Python package compatible with
PyTorch library - offering tools and methods dedicated to Open Set Recognition
in Deep Neural Networks. The package offers two state-of-the-art methods in the
field, a set of functions for handling base sets and generation of derived sets
for the Open Set Recognition task (where some classes are considered unknown
and used only in the testing process) and additional tools to handle datasets
and methods. The main goal of the package proposal is to simplify and promote
the correct experimental evaluation, where experiments are carried out on a
large number of derivative sets with various Openness and class-to-category
assignments. The authors hope that state-of-the-art methods available in the
package will become a source of a correct and open-source implementation of the
relevant solutions in the domain. | [
"cs.LG",
"cs.CV"
] | false |
2305.09647 | 2023-05-16T17:48:44Z | Wavelet-based Unsupervised Label-to-Image Translation | [
"George Eskandar",
"Mohamed Abdelsamad",
"Karim Armanious",
"Shuai Zhang",
"Bin Yang"
] | Semantic Image Synthesis (SIS) is a subclass of image-to-image translation
where a semantic layout is used to generate a photorealistic image.
State-of-the-art conditional Generative Adversarial Networks (GANs) need a huge
amount of paired data to accomplish this task while generic unpaired
image-to-image translation frameworks underperform in comparison, because they
color-code semantic layouts and learn correspondences in appearance instead of
semantic content. Starting from the assumption that a high quality generated
image should be segmented back to its semantic layout, we propose a new
Unsupervised paradigm for SIS (USIS) that makes use of a self-supervised
segmentation loss and whole image wavelet based discrimination. Furthermore, in
order to match the high-frequency distribution of real images, a novel
generator architecture in the wavelet domain is proposed. We test our
methodology on 3 challenging datasets and demonstrate its ability to bridge the
performance gap between paired and unpaired models. | [
"cs.CV",
"eess.IV"
] | false |
2305.09660 | 2023-05-16T17:58:29Z | Osteosarcoma Tumor Detection using Transfer Learning Models | [
"Raisa Fairooz Meem",
"Khandaker Tabin Hasan"
] | The field of clinical image analysis has been applying transfer learning
models increasingly due to their less computational complexity, better accuracy
etc. These are pre-trained models that don't require to be trained from scratch
which eliminates the necessity of large datasets. Transfer learning models are
mostly used for the analysis of brain, breast, or lung images but other sectors
such as bone marrow cell detection or bone cancer detection can also benefit
from using transfer learning models, especially considering the lack of
available large datasets for these tasks. This paper studies the performance of
several transfer learning models for osteosarcoma tumour detection.
Osteosarcoma is a type of bone cancer mostly found in the cells of the long
bones of the body. The dataset consists of H&E stained images divided into 4
categories- Viable Tumor, Non-viable Tumor, Non-Tumor and Viable Non-viable.
Both datasets were randomly divided into train and test sets following an 80-20
ratio. 80% was used for training and 20\% for test. 4 models are considered for
comparison- EfficientNetB7, InceptionResNetV2, NasNetLarge and ResNet50. All
these models are pre-trained on ImageNet. According to the result,
InceptionResNetV2 achieved the highest accuracy (93.29%), followed by
NasNetLarge (90.91%), ResNet50 (89.83%) and EfficientNetB7 (62.77%). It also
had the highest precision (0.8658) and recall (0.8658) values among the 4
models. | [
"eess.IV",
"cs.CV"
] | false |
2305.09662 | 2023-05-16T17:58:43Z | Make-An-Animation: Large-Scale Text-conditional 3D Human Motion
Generation | [
"Samaneh Azadi",
"Akbar Shah",
"Thomas Hayes",
"Devi Parikh",
"Sonal Gupta"
] | Text-guided human motion generation has drawn significant interest because of
its impactful applications spanning animation and robotics. Recently,
application of diffusion models for motion generation has enabled improvements
in the quality of generated motions. However, existing approaches are limited
by their reliance on relatively small-scale motion capture data, leading to
poor performance on more diverse, in-the-wild prompts. In this paper, we
introduce Make-An-Animation, a text-conditioned human motion generation model
which learns more diverse poses and prompts from large-scale image-text
datasets, enabling significant improvement in performance over prior works.
Make-An-Animation is trained in two stages. First, we train on a curated
large-scale dataset of (text, static pseudo-pose) pairs extracted from
image-text datasets. Second, we fine-tune on motion capture data, adding
additional layers to model the temporal dimension. Unlike prior diffusion
models for motion generation, Make-An-Animation uses a U-Net architecture
similar to recent text-to-video generation models. Human evaluation of motion
realism and alignment with input text shows that our model reaches
state-of-the-art performance on text-to-motion generation. | [
"cs.CV",
"cs.AI"
] | true |
2305.09746 | 2023-05-16T18:37:58Z | A Range-Null Space Decomposition Approach for Fast and Flexible Spectral
Compressive Imaging | [
"Junyu Wang",
"Shijie Wang",
"Ruijie Zhang",
"Zengqiang Zheng",
"Wenyu Liu",
"Xinggang Wang"
] | We present RND-SCI, a novel framework for compressive hyperspectral image
(HSI) reconstruction. Our framework decomposes the reconstructed object into
range-space and null-space components, where the range-space part ensures the
solution conforms to the compression process, and the null-space term
introduces a deep HSI prior to constraining the output to have satisfactory
properties. RND-SCI is not only simple in design with strong interpretability
but also can be easily adapted to various HSI reconstruction networks,
improving the quality of HSIs with minimal computational overhead. RND-SCI
significantly boosts the performance of HSI reconstruction networks in
retraining, fine-tuning or plugging into a pre-trained off-the-shelf model.
Based on the framework and SAUNet, we design an extremely fast HSI
reconstruction network, RND-SAUNet, which achieves an astounding 91 frames per
second while maintaining superior reconstruction accuracy compared to other
less time-consuming methods. Code and models are available at
https://github.com/hustvl/RND-SCI. | [
"cs.CV",
"eess.IV"
] | false |
2305.09833 | 2023-05-16T22:24:01Z | Segmentation of Aortic Vessel Tree in CT Scans with Deep Fully
Convolutional Networks | [
"Shaofeng Yuan",
"Feng Yang"
] | Automatic and accurate segmentation of aortic vessel tree (AVT) in computed
tomography (CT) scans is crucial for early detection, diagnosis and prognosis
of aortic diseases, such as aneurysms, dissections and stenosis. However, this
task remains challenges, due to the complexity of aortic vessel tree and amount
of CT angiography data. In this technical report, we use two-stage fully
convolutional networks (FCNs) to automatically segment AVT in CTA scans from
multiple centers. Specifically, we firstly adopt a 3D FCN with U-shape network
architecture to segment AVT in order to produce topology attention and
accelerate medical image analysis pipeline. And then another one 3D FCN is
trained to segment branches of AVT along the pseudo-centerline of AVT. In the
2023 MICCAI Segmentation of the Aorta (SEG.A.) Challenge , the reported method
was evaluated on the public dataset of 56 cases. The resulting Dice Similarity
Coefficient (DSC) is 0.920, Jaccard Similarity Coefficient (JSC) is 0.861,
Recall is 0.922, and Precision is 0.926 on a 5-fold random split of training
and validation set. | [
"eess.IV",
"cs.CV"
] | false |
2305.09847 | 2023-05-16T23:30:01Z | Selective Guidance: Are All the Denoising Steps of Guided Diffusion
Important? | [
"Pareesa Ameneh Golnari",
"Zhewei Yao",
"Yuxiong He"
] | This study examines the impact of optimizing the Stable Diffusion (SD) guided
inference pipeline. We propose optimizing certain denoising steps by limiting
the noise computation to conditional noise and eliminating unconditional noise
computation, thereby reducing the complexity of the target iterations by 50%.
Additionally, we demonstrate that later iterations of the SD are less sensitive
to optimization, making them ideal candidates for applying the suggested
optimization. Our experiments show that optimizing the last 20% of the
denoising loop iterations results in an 8.2% reduction in inference time with
almost no perceivable changes to the human eye. Furthermore, we found that by
extending the optimization to 50% of the last iterations, we can reduce
inference time by approximately 20.3%, while still generating visually pleasing
images. | [
"cs.LG",
"cs.CV"
] | false |
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