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2306.01489
|
2023-06-02T12:30:42Z
|
On Feature Diversity in Energy-based Models
|
[
"Firas Laakom",
"Jenni Raitoharju",
"Alexandros Iosifidis",
"Moncef Gabbouj"
] |
Energy-based learning is a powerful learning paradigm that encapsulates
various discriminative and generative approaches. An energy-based model (EBM)
is typically formed of inner-model(s) that learn a combination of the different
features to generate an energy mapping for each input configuration. In this
paper, we focus on the diversity of the produced feature set. We extend the
probably approximately correct (PAC) theory of EBMs and analyze the effect of
redundancy reduction on the performance of EBMs. We derive generalization
bounds for various learning contexts, i.e., regression, classification, and
implicit regression, with different energy functions and we show that indeed
reducing redundancy of the feature set can consistently decrease the gap
between the true and empirical expectation of the energy and boosts the
performance of the model.
|
[
"cs.LG",
"cs.IT",
"math.IT"
] | false |
2306.01494
|
2023-06-02T12:42:09Z
|
Local Message Passing on Frustrated Systems
|
[
"Luca Schmid",
"Joshua Brenk",
"Laurent Schmalen"
] |
Message passing on factor graphs is a powerful framework for probabilistic
inference, which finds important applications in various scientific domains.
The most wide-spread message passing scheme is the sum-product algorithm (SPA)
which gives exact results on trees but often fails on graphs with many small
cycles. We search for an alternative message passing algorithm that works
particularly well on such cyclic graphs. Therefore, we challenge the extrinsic
principle of the SPA, which loses its objective on graphs with cycles. We
further replace the local SPA message update rule at the factor nodes of the
underlying graph with a generic mapping, which is optimized in a data-driven
fashion. These modifications lead to a considerable improvement in performance
while preserving the simplicity of the SPA. We evaluate our method for two
classes of cyclic graphs: the 2x2 fully connected Ising grid and factor graphs
for symbol detection on linear communication channels with inter-symbol
interference. To enable the method for large graphs as they occur in practical
applications, we develop a novel loss function that is inspired by the Bethe
approximation from statistical physics and allows for training in an
unsupervised fashion.
|
[
"cs.LG",
"cs.IT",
"eess.SP",
"math.IT"
] | false |
2306.01570
|
2023-06-02T14:31:24Z
|
Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit
Commitment
|
[
"Arun Venkatesh Ramesh",
"Xingpeng Li"
] |
Security-constrained unit commitment (SCUC) is a computationally complex
process utilized in power system day-ahead scheduling and market clearing. SCUC
is run daily and requires state-of-the-art algorithms to speed up the process.
The constraints and data associated with SCUC are both geographically and
temporally correlated to ensure the reliability of the solution, which further
increases the complexity. In this paper, an advanced machine learning (ML)
model is used to study the patterns in power system historical data, which
inherently considers both spatial and temporal (ST) correlations in
constraints. The ST-correlated ML model is trained to understand spatial
correlation by considering graph neural networks (GNN) whereas temporal
sequences are studied using long short-term memory (LSTM) networks. The
proposed approach is validated on several test systems namely, IEEE 24-Bus
system, IEEE-73 Bus system, IEEE 118-Bus system, and synthetic South-Carolina
(SC) 500-Bus system. Moreover, B-{\theta} and power transfer distribution
factor (PTDF) based SCUC formulations were considered in this research.
Simulation results demonstrate that the ST approach can effectively predict
generator commitment schedule and classify critical and non-critical lines in
the system which are utilized for model reduction of SCUC to obtain
computational enhancement without loss in solution quality
|
[
"cs.LG",
"cs.SY",
"eess.SY",
"math.OC"
] | false |
2306.01603
|
2023-06-02T15:12:58Z
|
Decentralized Federated Learning: A Survey and Perspective
|
[
"Liangqi Yuan",
"Lichao Sun",
"Philip S. Yu",
"Ziran Wang"
] |
Federated learning (FL) has been gaining attention for its ability to share
knowledge while maintaining user data, protecting privacy, increasing learning
efficiency, and reducing communication overhead. Decentralized FL (DFL) is a
decentralized network architecture that eliminates the need for a central
server in contrast to centralized FL (CFL). DFL enables direct communication
between clients, resulting in significant savings in communication resources.
In this paper, a comprehensive survey and profound perspective is provided for
DFL. First, a review of the methodology, challenges, and variants of CFL is
conducted, laying the background of DFL. Then, a systematic and detailed
perspective on DFL is introduced, including iteration order, communication
protocols, network topologies, paradigm proposals, and temporal variability.
Next, based on the definition of DFL, several extended variants and
categorizations are proposed with state-of-the-art technologies. Lastly, in
addition to summarizing the current challenges in the DFL, some possible
solutions and future research directions are also discussed.
|
[
"cs.LG",
"cs.CY",
"cs.DC",
"cs.NI"
] | false |
2306.01638
|
2023-06-02T15:58:22Z
|
Do we become wiser with time? On causal equivalence with tiered
background knowledge
|
[
"Christine W. Bang",
"Vanessa Didelez"
] |
Equivalence classes of DAGs (represented by CPDAGs) may be too large to
provide useful causal information. Here, we address incorporating tiered
background knowledge yielding restricted equivalence classes represented by
'tiered MPDAGs'. Tiered knowledge leads to considerable gains in
informativeness and computational efficiency: We show that construction of
tiered MPDAGs only requires application of Meek's 1st rule, and that tiered
MPDAGs (unlike general MPDAGs) are chain graphs with chordal components. This
entails simplifications e.g. of determining valid adjustment sets for causal
effect estimation. Further, we characterise when one tiered ordering is more
informative than another, providing insights into useful aspects of background
knowledge.
|
[
"stat.ML",
"cs.LG",
"math.ST",
"stat.TH"
] | false |
2306.01674
|
2023-06-02T16:46:47Z
|
Neural Differential Recurrent Neural Network with Adaptive Time Steps
|
[
"Yixuan Tan",
"Liyan Xie",
"Xiuyuan Cheng"
] |
The neural Ordinary Differential Equation (ODE) model has shown success in
learning complex continuous-time processes from observations on discrete time
stamps. In this work, we consider the modeling and forecasting of time series
data that are non-stationary and may have sharp changes like spikes. We propose
an RNN-based model, called RNN-ODE-Adap, that uses a neural ODE to represent
the time development of the hidden states, and we adaptively select time steps
based on the steepness of changes of the data over time so as to train the
model more efficiently for the "spike-like" time series. Theoretically,
RNN-ODE-Adap yields provably a consistent estimation of the intensity function
for the Hawkes-type time series data. We also provide an approximation analysis
of the RNN-ODE model showing the benefit of adaptive steps. The proposed model
is demonstrated to achieve higher prediction accuracy with reduced
computational cost on simulated dynamic system data and point process data and
on a real electrocardiography dataset.
|
[
"stat.ML",
"cs.LG",
"cs.NA",
"math.NA"
] | false |
2306.01699
|
2023-06-02T17:18:20Z
|
Affinity Clustering Framework for Data Debiasing Using Pairwise
Distribution Discrepancy
|
[
"Siamak Ghodsi",
"Eirini Ntoutsi"
] |
Group imbalance, resulting from inadequate or unrepresentative data
collection methods, is a primary cause of representation bias in datasets.
Representation bias can exist with respect to different groups of one or more
protected attributes and might lead to prejudicial and discriminatory outcomes
toward certain groups of individuals; in cases where a learning model is
trained on such biased data. This paper presents MASC, a data augmentation
approach that leverages affinity clustering to balance the representation of
non-protected and protected groups of a target dataset by utilizing instances
of the same protected attributes from similar datasets that are categorized in
the same cluster as the target dataset by sharing instances of the protected
attribute. The proposed method involves constructing an affinity matrix by
quantifying distribution discrepancies between dataset pairs and transforming
them into a symmetric pairwise similarity matrix. A non-parametric spectral
clustering is then applied to this affinity matrix, automatically categorizing
the datasets into an optimal number of clusters. We perform a step-by-step
experiment as a demo of our method to show the procedure of the proposed data
augmentation method and evaluate and discuss its performance. A comparison with
other data augmentation methods, both pre- and post-augmentation, is conducted,
along with a model evaluation analysis of each method. Our method can handle
non-binary protected attributes so, in our experiments, bias is measured in a
non-binary protected attribute setup w.r.t. racial groups distribution for two
separate minority groups in comparison with the majority group before and after
debiasing. Empirical results imply that our method of augmenting dataset biases
using real (genuine) data from similar contexts can effectively debias the
target datasets comparably to existing data augmentation strategies.
|
[
"cs.LG",
"cs.AI",
"stat.ML"
] | false |
2306.01813
|
2023-06-02T09:04:45Z
|
Learning the effective order of a hypergraph dynamical system
|
[
"Leonie Neuhäuser",
"Michael Scholkemper",
"Francesco Tudisco",
"Michael T. Schaub"
] |
Dynamical systems on hypergraphs can display a rich set of behaviours not
observable for systems with pairwise interactions. Given a distributed
dynamical system with a putative hypergraph structure, an interesting question
is thus how much of this hypergraph structure is actually necessary to
faithfully replicate the observed dynamical behaviour. To answer this question,
we propose a method to determine the minimum order of a hypergraph necessary to
approximate the corresponding dynamics accurately. Specifically, we develop an
analytical framework that allows us to determine this order when the type of
dynamics is known. We utilize these ideas in conjunction with a hypergraph
neural network to directly learn the dynamics itself and the resulting order of
the hypergraph from both synthetic and real data sets consisting of observed
system trajectories.
|
[
"cs.LG",
"cs.SI",
"physics.soc-ph"
] | false |
2306.01814
|
2023-06-02T09:33:19Z
|
Fast Interactive Search with a Scale-Free Comparison Oracle
|
[
"Daniyar Chumbalov",
"Lars Klein",
"Lucas Maystre",
"Matthias Grossglauser"
] |
A comparison-based search algorithm lets a user find a target item $t$ in a
database by answering queries of the form, ``Which of items $i$ and $j$ is
closer to $t$?'' Instead of formulating an explicit query (such as one or
several keywords), the user navigates towards the target via a sequence of such
(typically noisy) queries.
We propose a scale-free probabilistic oracle model called $\gamma$-CKL for
such similarity triplets $(i,j;t)$, which generalizes the CKL triplet model
proposed in the literature. The generalization affords independent control over
the discriminating power of the oracle and the dimension of the feature space
containing the items.
We develop a search algorithm with provably exponential rate of convergence
under the $\gamma$-CKL oracle, thanks to a backtracking strategy that deals
with the unavoidable errors in updating the belief region around the target.
We evaluate the performance of the algorithm both over the posited oracle and
over several real-world triplet datasets. We also report on a comprehensive
user study, where human subjects navigate a database of face portraits.
|
[
"cs.IR",
"cs.HC",
"cs.LG"
] | false |
2306.01824
|
2023-06-02T14:13:50Z
|
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence
Alignment Generation
|
[
"Le Zhang",
"Jiayang Chen",
"Tao Shen",
"Yu Li",
"Siqi Sun"
] |
The field of protein folding research has been greatly advanced by deep
learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance
and atomic-level precision. As co-evolution is integral to protein structure
prediction, AF2's accuracy is significantly influenced by the depth of multiple
sequence alignment (MSA), which requires extensive exploration of a large
protein database for similar sequences. However, not all protein sequences
possess abundant homologous families, and consequently, AF2's performance can
degrade on such queries, at times failing to produce meaningful results. To
address this, we introduce a novel generative language model, MSA-Augmenter,
which leverages protein-specific attention mechanisms and large-scale MSAs to
generate useful, novel protein sequences not currently found in databases.
These sequences supplement shallow MSAs, enhancing the accuracy of structural
property predictions. Our experiments on CASP14 demonstrate that MSA-Augmenter
can generate de novo sequences that retain co-evolutionary information from
inferior MSAs, thereby improving protein structure prediction quality on top of
strong AF2.
|
[
"q-bio.QM",
"cs.CE",
"cs.LG",
"q-bio.BM"
] | false |
2306.01860
|
2023-06-02T18:29:07Z
|
No Bidding, No Regret: Pairwise-Feedback Mechanisms for Digital Goods
and Data Auctions
|
[
"Zachary Robertson",
"Oluwasanmi Koyejo"
] |
The growing demand for data and AI-generated digital goods, such as
personalized written content and artwork, necessitates effective pricing and
feedback mechanisms that account for uncertain utility and costly production.
Motivated by these developments, this study presents a novel mechanism design
addressing a general repeated-auction setting where the utility derived from a
sold good is revealed post-sale. The mechanism's novelty lies in using pairwise
comparisons for eliciting information from the bidder, arguably easier for
humans than assigning a numerical value. Our mechanism chooses allocations
using an epsilon-greedy strategy and relies on pairwise comparisons between
realized utility from allocated goods and an arbitrary value, avoiding the
learning-to-bid problem explored in previous work. We prove this mechanism to
be asymptotically truthful, individually rational, and welfare and revenue
maximizing. The mechanism's relevance is broad, applying to any setting with
made-to-order goods of variable quality. Experimental results on multi-label
toxicity annotation data, an example of negative utilities, highlight how our
proposed mechanism could enhance social welfare in data auctions. Overall, our
focus on human factors contributes to the development of more human-aware and
efficient mechanism design.
|
[
"cs.GT",
"cs.AI",
"cs.LG"
] | false |
2306.01864
|
2023-06-02T18:41:39Z
|
Discovering COVID-19 Coughing and Breathing Patterns from Unlabeled Data
Using Contrastive Learning with Varying Pre-Training Domains
|
[
"Jinjin Cai",
"Sudip Vhaduri",
"Xiao Luo"
] |
Rapid discovery of new diseases, such as COVID-19 can enable a timely
epidemic response, preventing the large-scale spread and protecting public
health. However, limited research efforts have been taken on this problem. In
this paper, we propose a contrastive learning-based modeling approach for
COVID-19 coughing and breathing pattern discovery from non-COVID coughs. To
validate our models, extensive experiments have been conducted using four large
audio datasets and one image dataset. We further explore the effects of
different factors, such as domain relevance and augmentation order on the
pre-trained models. Our results show that the proposed model can effectively
distinguish COVID-19 coughing and breathing from unlabeled data and labeled
non-COVID coughs with an accuracy of up to 0.81 and 0.86, respectively.
Findings from this work will guide future research to detect an outbreak of a
new disease early.
|
[
"cs.LG",
"cs.SD",
"eess.AS"
] | false |
2306.01906
|
2023-06-02T20:31:33Z
|
Synaptic motor adaptation: A three-factor learning rule for adaptive
robotic control in spiking neural networks
|
[
"Samuel Schmidgall",
"Joe Hays"
] |
Legged robots operating in real-world environments must possess the ability
to rapidly adapt to unexpected conditions, such as changing terrains and
varying payloads. This paper introduces the Synaptic Motor Adaptation (SMA)
algorithm, a novel approach to achieving real-time online adaptation in
quadruped robots through the utilization of neuroscience-derived rules of
synaptic plasticity with three-factor learning. To facilitate rapid adaptation,
we meta-optimize a three-factor learning rule via gradient descent to adapt to
uncertainty by approximating an embedding produced by privileged information
using only locally accessible onboard sensing data. Our algorithm performs
similarly to state-of-the-art motor adaptation algorithms and presents a clear
path toward achieving adaptive robotics with neuromorphic hardware.
|
[
"cs.RO",
"cs.AI",
"cs.LG",
"cs.NE"
] | false |
2306.01914
|
2023-06-02T20:43:38Z
|
Smooth Model Predictive Control with Applications to Statistical
Learning
|
[
"Kwangjun Ahn",
"Daniel Pfrommer",
"Jack Umenberger",
"Tobia Marcucci",
"Zak Mhammedi",
"Ali Jadbabaie"
] |
Statistical learning theory and high dimensional statistics have had a
tremendous impact on Machine Learning theory and have impacted a variety of
domains including systems and control theory. Over the past few years we have
witnessed a variety of applications of such theoretical tools to help answer
questions such as: how many state-action pairs are needed to learn a static
control policy to a given accuracy? Recent results have shown that continuously
differentiable and stabilizing control policies can be well-approximated using
neural networks with hard guarantees on performance, yet often even the
simplest constrained control problems are not smooth. To address this void, in
this paper we study smooth approximations of linear Model Predictive Control
(MPC) policies, in which hard constraints are replaced by barrier functions,
a.k.a. barrier MPC. In particular, we show that barrier MPC inherits the
exponential stability properties of the original non-smooth MPC policy. Using a
careful analysis of the proposed barrier MPC, we show that its smoothness
constant can be carefully controlled, thereby paving the way for new sample
complexity results for approximating MPC policies from sampled state-action
pairs.
|
[
"eess.SY",
"cs.LG",
"cs.SY"
] | false |
2306.01916
|
2023-06-02T21:02:51Z
|
In-the-wild Speech Emotion Conversion Using Disentangled Self-Supervised
Representations and Neural Vocoder-based Resynthesis
|
[
"Navin Raj Prabhu",
"Nale Lehmann-Willenbrock",
"Timo Gerkmann"
] |
Speech emotion conversion aims to convert the expressed emotion of a spoken
utterance to a target emotion while preserving the lexical information and the
speaker's identity. In this work, we specifically focus on in-the-wild emotion
conversion where parallel data does not exist, and the problem of disentangling
lexical, speaker, and emotion information arises. In this paper, we introduce a
methodology that uses self-supervised networks to disentangle the lexical,
speaker, and emotional content of the utterance, and subsequently uses a
HiFiGAN vocoder to resynthesise the disentangled representations to a speech
signal of the targeted emotion. For better representation and to achieve
emotion intensity control, we specifically focus on the aro\-usal dimension of
continuous representations, as opposed to performing emotion conversion on
categorical representations. We test our methodology on the large in-the-wild
MSP-Podcast dataset. Results reveal that the proposed approach is aptly
conditioned on the emotional content of input speech and is capable of
synthesising natural-sounding speech for a target emotion. Results further
reveal that the methodology better synthesises speech for mid-scale arousal (2
to 6) than for extreme arousal (1 and 7).
|
[
"eess.AS",
"cs.HC",
"cs.LG"
] | false |
2306.01920
|
2023-06-02T21:22:27Z
|
Context-Aware Bayesian Network Actor-Critic Methods for Cooperative
Multi-Agent Reinforcement Learning
|
[
"Dingyang Chen",
"Qi Zhang"
] |
Executing actions in a correlated manner is a common strategy for human
coordination that often leads to better cooperation, which is also potentially
beneficial for cooperative multi-agent reinforcement learning (MARL). However,
the recent success of MARL relies heavily on the convenient paradigm of purely
decentralized execution, where there is no action correlation among agents for
scalability considerations. In this work, we introduce a Bayesian network to
inaugurate correlations between agents' action selections in their joint
policy. Theoretically, we establish a theoretical justification for why action
dependencies are beneficial by deriving the multi-agent policy gradient formula
under such a Bayesian network joint policy and proving its global convergence
to Nash equilibria under tabular softmax policy parameterization in cooperative
Markov games. Further, by equipping existing MARL algorithms with a recent
method of differentiable directed acyclic graphs (DAGs), we develop practical
algorithms to learn the context-aware Bayesian network policies in scenarios
with partial observability and various difficulty. We also dynamically decrease
the sparsity of the learned DAG throughout the training process, which leads to
weakly or even purely independent policies for decentralized execution.
Empirical results on a range of MARL benchmarks show the benefits of our
approach.
|
[
"cs.MA",
"cs.AI",
"cs.GT",
"cs.LG"
] | false |
2306.01926
|
2023-06-02T21:45:13Z
|
RITA: Group Attention is All You Need for Timeseries Analytics
|
[
"Jiaming Liang",
"Lei Cao",
"Samuel Madden",
"Zachary Ives",
"Guoliang Li"
] |
Timeseries analytics is of great importance in many real-world applications.
Recently, the Transformer model, popular in natural language processing, has
been leveraged to learn high quality feature embeddings from timeseries, core
to the performance of various timeseries analytics tasks. However, the
quadratic time and space complexities limit Transformers' scalability,
especially for long timeseries. To address these issues, we develop a
timeseries analytics tool, RITA, which uses a novel attention mechanism, named
group attention, to address this scalability issue. Group attention dynamically
clusters the objects based on their similarity into a small number of groups
and approximately computes the attention at the coarse group granularity. It
thus significantly reduces the time and space complexity, yet provides a
theoretical guarantee on the quality of the computed attention. The dynamic
scheduler of RITA continuously adapts the number of groups and the batch size
in the training process, ensuring group attention always uses the fewest groups
needed to meet the approximation quality requirement. Extensive experiments on
various timeseries datasets and analytics tasks demonstrate that RITA
outperforms the state-of-the-art in accuracy and is significantly faster --
with speedups of up to 63X.
|
[
"cs.LG",
"cs.AI",
"cs.DB"
] | false |
2306.01212
|
2023-06-02T00:24:42Z
|
Linked Deep Gaussian Process Emulation for Model Networks
|
[
"Deyu Ming",
"Daniel Williamson"
] |
Modern scientific problems are often multi-disciplinary and require
integration of computer models from different disciplines, each with distinct
functional complexities, programming environments, and computation times.
Linked Gaussian process (LGP) emulation tackles this challenge through a
divide-and-conquer strategy that integrates Gaussian process emulators of the
individual computer models in a network. However, the required stationarity of
the component Gaussian process emulators within the LGP framework limits its
applicability in many real-world applications. In this work, we conceptualize a
network of computer models as a deep Gaussian process with partial exposure of
its hidden layers. We develop a method for inference for these partially
exposed deep networks that retains a key strength of the LGP framework, whereby
each model can be emulated separately using a DGP and then linked together. We
show in both synthetic and empirical examples that our linked deep Gaussian
process emulators exhibit significantly better predictive performance than
standard LGP emulators in terms of accuracy and uncertainty quantification.
They also outperform single DGPs fitted to the network as a whole because they
are able to integrate information from the partially exposed hidden layers. Our
methods are implemented in an R package $\texttt{dgpsi}$ that is freely
available on CRAN.
|
[
"stat.ML",
"cs.LG",
"stat.AP",
"stat.CO",
"stat.ME"
] | false |
2306.01485
|
2023-06-02T12:22:35Z
|
Robust low-rank training via approximate orthonormal constraints
|
[
"Dayana Savostianova",
"Emanuele Zangrando",
"Gianluca Ceruti",
"Francesco Tudisco"
] |
With the growth of model and data sizes, a broad effort has been made to
design pruning techniques that reduce the resource demand of deep learning
pipelines, while retaining model performance. In order to reduce both inference
and training costs, a prominent line of work uses low-rank matrix
factorizations to represent the network weights. Although able to retain
accuracy, we observe that low-rank methods tend to compromise model robustness
against adversarial perturbations. By modeling robustness in terms of the
condition number of the neural network, we argue that this loss of robustness
is due to the exploding singular values of the low-rank weight matrices. Thus,
we introduce a robust low-rank training algorithm that maintains the network's
weights on the low-rank matrix manifold while simultaneously enforcing
approximate orthonormal constraints. The resulting model reduces both training
and inference costs while ensuring well-conditioning and thus better
adversarial robustness, without compromising model accuracy. This is shown by
extensive numerical evidence and by our main approximation theorem that shows
the computed robust low-rank network well-approximates the ideal full model,
provided a highly performing low-rank sub-network exists.
|
[
"cs.LG",
"cs.AI",
"cs.NA",
"math.NA",
"stat.ML"
] | false |
2306.01988
|
2023-06-03T03:21:18Z
|
Lightweight Structure-aware Transformer Network for VHR Remote Sensing
Image Change Detection
|
[
"Tao Lei",
"Yetong Xu",
"Hailong Ning",
"Zhiyong Lv",
"Chongdan Min",
"Yaochu Jin",
"Asoke K. Nandi"
] |
Popular Transformer networks have been successfully applied to remote sensing
(RS) image change detection (CD) identifications and achieve better results
than most convolutional neural networks (CNNs), but they still suffer from two
main problems. First, the computational complexity of the Transformer grows
quadratically with the increase of image spatial resolution, which is
unfavorable to very high-resolution (VHR) RS images. Second, these popular
Transformer networks tend to ignore the importance of fine-grained features,
which results in poor edge integrity and internal tightness for largely changed
objects and leads to the loss of small changed objects. To address the above
issues, this Letter proposes a Lightweight Structure-aware Transformer (LSAT)
network for RS image CD. The proposed LSAT has two advantages. First, a
Cross-dimension Interactive Self-attention (CISA) module with linear complexity
is designed to replace the vanilla self-attention in visual Transformer, which
effectively reduces the computational complexity while improving the feature
representation ability of the proposed LSAT. Second, a Structure-aware
Enhancement Module (SAEM) is designed to enhance difference features and edge
detail information, which can achieve double enhancement by difference
refinement and detail aggregation so as to obtain fine-grained features of
bi-temporal RS images. Experimental results show that the proposed LSAT
achieves significant improvement in detection accuracy and offers a better
tradeoff between accuracy and computational costs than most state-of-the-art CD
methods for VHR RS images.
|
[
"cs.CV"
] | false |
2306.02021
|
2023-06-03T06:34:17Z
|
Towards Black-box Adversarial Example Detection: A Data
Reconstruction-based Method
|
[
"Yifei Gao",
"Zhiyu Lin",
"Yunfan Yang",
"Jitao Sang"
] |
Adversarial example detection is known to be an effective adversarial defense
method. Black-box attack, which is a more realistic threat and has led to
various black-box adversarial training-based defense methods, however, does not
attract considerable attention in adversarial example detection. In this paper,
we fill this gap by positioning the problem of black-box adversarial example
detection (BAD). Data analysis under the introduced BAD settings demonstrates
(1) the incapability of existing detectors in addressing the black-box scenario
and (2) the potential of exploring BAD solutions from a data perspective. To
tackle the BAD problem, we propose a data reconstruction-based adversarial
example detection method. Specifically, we use variational auto-encoder (VAE)
to capture both pixel and frequency representations of normal examples. Then we
use reconstruction error to detect adversarial examples. Compared with existing
detection methods, the proposed method achieves substantially better detection
performance in BAD, which helps promote the deployment of adversarial example
detection-based defense solutions in real-world models.
|
[
"cs.CV"
] | false |
2306.02061
|
2023-06-03T09:19:24Z
|
Balancing Logit Variation for Long-tailed Semantic Segmentation
|
[
"Yuchao Wang",
"Jingjing Fei",
"Haochen Wang",
"Wei Li",
"Tianpeng Bao",
"Liwei Wu",
"Rui Zhao",
"Yujun Shen"
] |
Semantic segmentation usually suffers from a long-tail data distribution. Due
to the imbalanced number of samples across categories, the features of those
tail classes may get squeezed into a narrow area in the feature space. Towards
a balanced feature distribution, we introduce category-wise variation into the
network predictions in the training phase such that an instance is no longer
projected to a feature point, but a small region instead. Such a perturbation
is highly dependent on the category scale, which appears as assigning smaller
variation to head classes and larger variation to tail classes. In this way, we
manage to close the gap between the feature areas of different categories,
resulting in a more balanced representation. It is noteworthy that the
introduced variation is discarded at the inference stage to facilitate a
confident prediction. Although with an embarrassingly simple implementation,
our method manifests itself in strong generalizability to various datasets and
task settings. Extensive experiments suggest that our plug-in design lends
itself well to a range of state-of-the-art approaches and boosts the
performance on top of them.
|
[
"cs.CV"
] | false |
2306.02064
|
2023-06-03T09:36:16Z
|
Flew Over Learning Trap: Learn Unlearnable Samples by Progressive Staged
Training
|
[
"Pucheng Dang",
"Xing Hu",
"Kaidi Xu",
"Jinhao Duan",
"Di Huang",
"Husheng Han",
"Rui Zhang",
"Zidong Du",
"Qi Guo",
"Yunji Chen"
] |
Unlearning techniques are proposed to prevent third parties from exploiting
unauthorized data, which generate unlearnable samples by adding imperceptible
perturbations to data for public publishing. These unlearnable samples
effectively misguide model training to learn perturbation features but ignore
image semantic features. We make the in-depth analysis and observe that models
can learn both image features and perturbation features of unlearnable samples
at an early stage, but rapidly go to the overfitting stage since the shallow
layers tend to overfit on perturbation features and make models fall into
overfitting quickly. Based on the observations, we propose Progressive Staged
Training to effectively prevent models from overfitting in learning
perturbation features. We evaluated our method on multiple model architectures
over diverse datasets, e.g., CIFAR-10, CIFAR-100, and ImageNet-mini. Our method
circumvents the unlearnability of all state-of-the-art methods in the
literature and provides a reliable baseline for further evaluation of
unlearnable techniques.
|
[
"cs.CV"
] | false |
2306.02083
|
2023-06-03T11:08:38Z
|
Efficient Text-Guided 3D-Aware Portrait Generation with Score
Distillation Sampling on Distribution
|
[
"Yiji Cheng",
"Fei Yin",
"Xiaoke Huang",
"Xintong Yu",
"Jiaxiang Liu",
"Shikun Feng",
"Yujiu Yang",
"Yansong Tang"
] |
Text-to-3D is an emerging task that allows users to create 3D content with
infinite possibilities. Existing works tackle the problem by optimizing a 3D
representation with guidance from pre-trained diffusion models. An apparent
drawback is that they need to optimize from scratch for each prompt, which is
computationally expensive and often yields poor visual fidelity. In this paper,
we propose DreamPortrait, which aims to generate text-guided 3D-aware portraits
in a single-forward pass for efficiency. To achieve this, we extend Score
Distillation Sampling from datapoint to distribution formulation, which injects
semantic prior into a 3D distribution. However, the direct extension will lead
to the mode collapse problem since the objective only pursues semantic
alignment. Hence, we propose to optimize a distribution with hierarchical
condition adapters and GAN loss regularization. For better 3D modeling, we
further design a 3D-aware gated cross-attention mechanism to explicitly let the
model perceive the correspondence between the text and the 3D-aware space.
These elaborated designs enable our model to generate portraits with robust
multi-view semantic consistency, eliminating the need for optimization-based
methods. Extensive experiments demonstrate our model's highly competitive
performance and significant speed boost against existing methods.
|
[
"cs.CV"
] | false |
2306.02092
|
2023-06-03T11:50:44Z
|
Relieving Triplet Ambiguity: Consensus Network for Language-Guided Image
Retrieval
|
[
"Xu Zhang",
"Zhedong Zheng",
"Xiaohan Wang",
"Yi Yang"
] |
Language-guided image retrieval enables users to search for images and
interact with the retrieval system more naturally and expressively by using a
reference image and a relative caption as a query. Most existing studies mainly
focus on designing image-text composition architecture to extract
discriminative visual-linguistic relations. Despite great success, we identify
an inherent problem that obstructs the extraction of discriminative features
and considerably compromises model training: \textbf{triplet ambiguity}. This
problem stems from the annotation process wherein annotators view only one
triplet at a time. As a result, they often describe simple attributes, such as
color, while neglecting fine-grained details like location and style. This
leads to multiple false-negative candidates matching the same modification
text. We propose a novel Consensus Network (Css-Net) that self-adaptively
learns from noisy triplets to minimize the negative effects of triplet
ambiguity. Inspired by the psychological finding that groups perform better
than individuals, Css-Net comprises 1) a consensus module featuring four
distinct compositors that generate diverse fused image-text embeddings and 2) a
Kullback-Leibler divergence loss, which fosters learning among the compositors,
enabling them to reduce biases learned from noisy triplets and reach a
consensus. The decisions from four compositors are weighted during evaluation
to further achieve consensus. Comprehensive experiments on three datasets
demonstrate that Css-Net can alleviate triplet ambiguity, achieving competitive
performance on benchmarks, such as $+2.77\%$ R@10 and $+6.67\%$ R@50 on
FashionIQ.
|
[
"cs.CV"
] | false |
2306.02094
|
2023-06-03T11:54:56Z
|
Segment Anything Meets Semantic Communication
|
[
"Shehbaz Tariq",
"Brian Estadimas Arfeto",
"Chaoning Zhang",
"Hyundong Shin"
] |
In light of the diminishing returns of traditional methods for enhancing
transmission rates, the domain of semantic communication presents promising new
frontiers. Focusing on image transmission, this paper explores the application
of foundation models, particularly the Segment Anything Model (SAM) developed
by Meta AI Research, to improve semantic communication. SAM is a promptable
image segmentation model that has gained attention for its ability to perform
zero-shot segmentation tasks without explicit training or domain-specific
knowledge. By employing SAM's segmentation capability and lightweight neural
network architecture for semantic coding, we propose a practical approach to
semantic communication. We demonstrate that this approach retains critical
semantic features, achieving higher image reconstruction quality and reducing
communication overhead. This practical solution eliminates the
resource-intensive stage of training a segmentation model and can be applied to
any semantic coding architecture, paving the way for real-world applications.
|
[
"cs.CV"
] | false |
2306.02095
|
2023-06-03T12:05:07Z
|
Content-aware Token Sharing for Efficient Semantic Segmentation with
Vision Transformers
|
[
"Chenyang Lu",
"Daan de Geus",
"Gijs Dubbelman"
] |
This paper introduces Content-aware Token Sharing (CTS), a token reduction
approach that improves the computational efficiency of semantic segmentation
networks that use Vision Transformers (ViTs). Existing works have proposed
token reduction approaches to improve the efficiency of ViT-based image
classification networks, but these methods are not directly applicable to
semantic segmentation, which we address in this work. We observe that, for
semantic segmentation, multiple image patches can share a token if they contain
the same semantic class, as they contain redundant information. Our approach
leverages this by employing an efficient, class-agnostic policy network that
predicts if image patches contain the same semantic class, and lets them share
a token if they do. With experiments, we explore the critical design choices of
CTS and show its effectiveness on the ADE20K, Pascal Context and Cityscapes
datasets, various ViT backbones, and different segmentation decoders. With
Content-aware Token Sharing, we are able to reduce the number of processed
tokens by up to 44%, without diminishing the segmentation quality.
|
[
"cs.CV"
] | false |
2306.02098
|
2023-06-03T12:15:20Z
|
Towards Complex Real-World Safety Factory Inspection: A High-Quality
Dataset for Safety Clothing and Helmet Detection
|
[
"Fusheng Yu",
"Xiaoping Wang",
"Jiang Li",
"Shaojin Wu",
"Junjie Zhang",
"Zhigang Zeng"
] |
Safety clothing and helmets play a crucial role in ensuring worker safety at
construction sites. Recently, deep learning methods have garnered significant
attention in the field of computer vision for their potential to enhance safety
and efficiency in various industries. However, limited availability of
high-quality datasets has hindered the development of deep learning methods for
safety clothing and helmet detection. In this work, we present a large,
comprehensive, and realistic high-quality dataset for safety clothing and
helmet detection, which was collected from a real-world chemical plant and
annotated by professional security inspectors. Our dataset has been compared
with several existing open-source datasets, and its effectiveness has been
verified applying some classic object detection methods. The results
demonstrate that our dataset is more complete and performs better in real-world
settings. Furthermore, we have released our deployment code to the public to
encourage the adoption of our dataset and improve worker safety. We hope that
our efforts will promote the convergence of academic research and industry,
ultimately contribute to the betterment of society.
|
[
"cs.CV"
] | false |
2306.02142
|
2023-06-03T15:56:30Z
|
TransDocAnalyser: A Framework for Offline Semi-structured Handwritten
Document Analysis in the Legal Domain
|
[
"Sagar Chakraborty",
"Gaurav Harit",
"Saptarshi Ghosh"
] |
State-of-the-art offline Optical Character Recognition (OCR) frameworks
perform poorly on semi-structured handwritten domain-specific documents due to
their inability to localize and label form fields with domain-specific
semantics. Existing techniques for semi-structured document analysis have
primarily used datasets comprising invoices, purchase orders, receipts, and
identity-card documents for benchmarking. In this work, we build the first
semi-structured document analysis dataset in the legal domain by collecting a
large number of First Information Report (FIR) documents from several police
stations in India. This dataset, which we call the FIR dataset, is more
challenging than most existing document analysis datasets, since it combines a
wide variety of handwritten text with printed text. We also propose an
end-to-end framework for offline processing of handwritten semi-structured
documents, and benchmark it on our novel FIR dataset. Our framework used
Encoder-Decoder architecture for localizing and labelling the form fields and
for recognizing the handwritten content. The encoder consists of Faster-RCNN
and Vision Transformers. Further the Transformer-based decoder architecture is
trained with a domain-specific tokenizer. We also propose a post-correction
method to handle recognition errors pertaining to the domain-specific terms.
Our proposed framework achieves state-of-the-art results on the FIR dataset
outperforming several existing models
|
[
"cs.CV",
"I.2.1"
] | false |
2311.11465
|
2023-06-03T08:06:38Z
|
Understanding Segment Anything Model: SAM is Biased Towards Texture
Rather than Shape
|
[
"Chaoning Zhang",
"Yu Qiao",
"Shehbaz Tariq",
"Sheng Zheng",
"Chenshuang Zhang",
"Chenghao Li",
"Hyundong Shin",
"Choong Seon Hong"
] |
In contrast to the human vision that mainly depends on the shape for
recognizing the objects, deep image recognition models are widely known to be
biased toward texture. Recently, Meta research team has released the first
foundation model for image segmentation, termed segment anything model (SAM),
which has attracted significant attention. In this work, we understand SAM from
the perspective of texture \textit{v.s.} shape. Different from label-oriented
recognition tasks, the SAM is trained to predict a mask for covering the object
shape based on a promt. With this said, it seems self-evident that the SAM is
biased towards shape. In this work, however, we reveal an interesting finding:
the SAM is strongly biased towards texture-like dense features rather than
shape. This intriguing finding is supported by a novel setup where we
disentangle texture and shape cues and design texture-shape cue conflict for
mask prediction.
|
[
"cs.CV"
] | false |
2306.01983
|
2023-06-03T02:33:38Z
|
Mitigating Backdoor Attack Via Prerequisite Transformation
|
[
"Han Gao"
] |
In recent years, with the successful application of DNN in fields such as NLP
and CV, its security has also received widespread attention. (Author) proposed
the method of backdoor attack in Badnet. Switch implanted backdoor into the
model by poisoning the training samples. The model with backdoor did not
exhibit any abnormalities on the normal validation sample set, but in the input
with trigger, they were mistakenly classified as the attacker's designated
category or randomly classified as a different category from the ground truth,
This attack method seriously threatens the normal application of DNN in real
life, such as autonomous driving, object detection, etc.This article proposes a
new method to combat backdoor attacks. We refer to the features in the area
covered by the trigger as trigger features, and the remaining areas as normal
features. By introducing prerequisite calculation conditions during the
training process, these conditions have little impact on normal features and
trigger features, and can complete the training of a standard backdoor model.
The model trained under these prerequisite calculation conditions can, In the
verification set D'val with the same premise calculation conditions, the
performance is consistent with that of the ordinary backdoor model. However, in
the verification set Dval without the premise calculation conditions, the
verification accuracy decreases very little (7%~12%), while the attack success
rate (ASR) decreases from 90% to about 8%.Author call this method Prerequisite
Transformation(PT).
|
[
"cs.CR",
"cs.CV"
] | false |
2306.02133
|
2023-06-03T15:06:12Z
|
Graph Mover's Distance: An Efficiently Computable Distance Measure for
Geometric Graphs
|
[
"Sushovan Majhi"
] |
Many applications in pattern recognition represent patterns as a geometric
graph. The geometric graph distance (GGD) has recently been studied as a
meaningful measure of similarity between two geometric graphs. Since computing
the GGD is known to be $\mathcal{NP}$-hard, the distance measure proves an
impractical choice for applications. As a computationally tractable
alternative, we propose in this paper the Graph Mover's Distance (GMD), which
has been formulated as an instance of the earth mover's distance. The
computation of the GMD between two geometric graphs with at most $n$ vertices
takes only $O(n^3)$-time. Alongside studying the metric properties of the GMD,
we investigate the stability of the GGD and GMD. The GMD also demonstrates
extremely promising empirical evidence at recognizing letter drawings from the
{\tt LETTER} dataset \cite{da_vitoria_lobo_iam_2008}.
|
[
"cs.CG",
"cs.CV"
] | false |
2306.02143
|
2023-06-03T15:58:38Z
|
Hierarchical Multiresolution Feature- and Prior-based Graphs for
Classification
|
[
"Faezeh Fallah"
] |
To incorporate spatial (neighborhood) and bidirectional hierarchical
relationships as well as features and priors of the samples into their
classification, we formulated the classification problem on three variants of
multiresolution neighborhood graphs and the graph of a hierarchical conditional
random field. Each of these graphs was weighted and undirected and could thus
incorporate the spatial or hierarchical relationships in all directions. In
addition, each variant of the proposed neighborhood graphs was composed of a
spatial feature-based subgraph and an aspatial prior-based subgraph. It
expanded on a random walker graph by using novel mechanisms to derive the edge
weights of its spatial feature-based subgraph. These mechanisms included
implicit and explicit edge detection to enhance detection of weak boundaries
between different classes in spatial domain. The implicit edge detection relied
on the outlier detection capability of the Tukey's function and the
classification reliabilities of the samples estimated by a hierarchical random
forest classifier. Similar mechanism was used to derive the edge weights and
thus the energy function of the hierarchical conditional random field. This
way, the classification problem boiled down to a system of linear equations and
a minimization of the energy function which could be done via fast and
efficient techniques.
|
[
"cs.CV",
"cs.LG"
] | false |
2306.02216
|
2023-06-03T23:53:57Z
|
Forgettable Federated Linear Learning with Certified Data Removal
|
[
"Ruinan Jin",
"Minghui Chen",
"Qiong Zhang",
"Xiaoxiao Li"
] |
Federated learning (FL) is a trending distributed learning framework that
enables collaborative model training without data sharing. Machine learning
models trained on datasets can potentially expose the private information of
the training data, revealing details about individual data records. In this
study, we focus on the FL paradigm that grants clients the ``right to be
forgotten''. The forgettable FL framework should bleach its global model
weights as it has never seen that client and hence does not reveal any
information about the client. To this end, we propose the Forgettable Federated
Linear Learning (2F2L) framework featured with novel training and data removal
strategies. The training pipeline, named Federated linear training, employs
linear approximation on the model parameter space to enable our 2F2L framework
work for deep neural networks while achieving comparable results with canonical
neural network training. We also introduce FedRemoval, an efficient and
effective removal strategy that tackles the computational challenges in FL by
approximating the Hessian matrix using public server data from the pretrained
model. Unlike the previous uncertified and heuristic machine unlearning methods
in FL, we provide theoretical guarantees by bounding the differences of model
weights by our FedRemoval and that from retraining from scratch. Experimental
results on MNIST and Fashion-MNIST datasets demonstrate the effectiveness of
our method in achieving a balance between model accuracy and information
removal, outperforming baseline strategies and approaching retraining from
scratch.
|
[
"cs.LG",
"cs.CV"
] | false |
2308.05178
|
2023-06-03T12:19:21Z
|
An Improved Model for Diabetic Retinopathy Detection by using Transfer
Learning and Ensemble Learning
|
[
"Md. Simul Hasan Talukder",
"Ajay Kirshno Sarkar",
"Sharmin Akter",
"Md. Nuhi-Alamin"
] |
Diabetic Retinopathy (DR) is an ocular condition caused by a sustained high
level of sugar in the blood, which causes the retinal capillaries to block and
bleed, causing retinal tissue damage. It usually results in blindness. Early
detection can help in lowering the risk of DR and its severity. The robust and
accurate prediction and detection of diabetic retinopathy is a challenging
task. This paper develops a machine learning model for detecting Diabetic
Retinopathy that is entirely accurate. Pre-trained models such as ResNet50,
InceptionV3, Xception, DenseNet121, VGG19, NASNetMobile, MobileNetV2,
DensNet169, and DenseNet201 with pooling layer, dense layer, and appropriate
dropout layer at the bottom of them were carried out in transfer learning (TL)
approach. Data augmentation and regularization was performed to reduce
overfitting. Transfer Learning model of DenseNet121, Average and weighted
ensemble of DenseNet169 and DenseNet201 TL architectures contribute
individually the highest accuracy of 100%, the highest precision, recall, F-1
score of 100%, 100%, and 100%, respectively.
|
[
"eess.IV",
"cs.CV"
] | false |
2306.01970
|
2023-06-03T00:38:40Z
|
Temporal-spatial Correlation Attention Network for Clinical Data
Analysis in Intensive Care Unit
|
[
"Weizhi Nie",
"Yuhe Yu",
"Chen Zhang",
"Dan Song",
"Lina Zhao",
"Yunpeng Bai"
] |
In recent years, medical information technology has made it possible for
electronic health record (EHR) to store fairly complete clinical data. This has
brought health care into the era of "big data". However, medical data are often
sparse and strongly correlated, which means that medical problems cannot be
solved effectively. With the rapid development of deep learning in recent
years, it has provided opportunities for the use of big data in healthcare. In
this paper, we propose a temporal-saptial correlation attention network (TSCAN)
to handle some clinical characteristic prediction problems, such as predicting
death, predicting length of stay, detecting physiologic decline, and
classifying phenotypes. Based on the design of the attention mechanism model,
our approach can effectively remove irrelevant items in clinical data and
irrelevant nodes in time according to different tasks, so as to obtain more
accurate prediction results. Our method can also find key clinical indicators
of important outcomes that can be used to improve treatment options. Our
experiments use information from the Medical Information Mart for Intensive
Care (MIMIC-IV) database, which is open to the public. Finally, we have
achieved significant performance benefits of 2.0\% (metric) compared to other
SOTA prediction methods. We achieved a staggering 90.7\% on mortality rate,
45.1\% on length of stay. The source code can be find:
\url{https://github.com/yuyuheintju/TSCAN}.
|
[
"cs.LG",
"cs.AI",
"cs.CV",
"cs.CY"
] | false |
2306.05500
|
2023-06-03T21:39:07Z
|
Word-Level Explanations for Analyzing Bias in Text-to-Image Models
|
[
"Alexander Lin",
"Lucas Monteiro Paes",
"Sree Harsha Tanneru",
"Suraj Srinivas",
"Himabindu Lakkaraju"
] |
Text-to-image models take a sentence (i.e., prompt) and generate images
associated with this input prompt. These models have created award wining-art,
videos, and even synthetic datasets. However, text-to-image (T2I) models can
generate images that underrepresent minorities based on race and sex. This
paper investigates which word in the input prompt is responsible for bias in
generated images. We introduce a method for computing scores for each word in
the prompt; these scores represent its influence on biases in the model's
output. Our method follows the principle of \emph{explaining by removing},
leveraging masked language models to calculate the influence scores. We perform
experiments on Stable Diffusion to demonstrate that our method identifies the
replication of societal stereotypes in generated images.
|
[
"cs.CL",
"cs.AI",
"cs.CV",
"cs.LG"
] | false |
2306.02022
|
2023-06-03T06:42:17Z
|
ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for
Benchmarking Automatic Visit Note Generation
|
[
"Wen-wai Yim",
"Yujuan Fu",
"Asma Ben Abacha",
"Neal Snider",
"Thomas Lin",
"Meliha Yetisgen"
] |
Recent immense breakthroughs in generative models such as in GPT4 have
precipitated re-imagined ubiquitous usage of these models in all applications.
One area that can benefit by improvements in artificial intelligence (AI) is
healthcare. The note generation task from doctor-patient encounters, and its
associated electronic medical record documentation, is one of the most arduous
time-consuming tasks for physicians. It is also a natural prime potential
beneficiary to advances in generative models. However with such advances,
benchmarking is more critical than ever. Whether studying model weaknesses or
developing new evaluation metrics, shared open datasets are an imperative part
of understanding the current state-of-the-art. Unfortunately as clinic
encounter conversations are not routinely recorded and are difficult to
ethically share due to patient confidentiality, there are no sufficiently large
clinic dialogue-note datasets to benchmark this task. Here we present the
Ambient Clinical Intelligence Benchmark (ACI-BENCH) corpus, the largest dataset
to date tackling the problem of AI-assisted note generation from visit
dialogue. We also present the benchmark performances of several common
state-of-the-art approaches.
|
[
"cs.CL"
] | false |
2306.02042
|
2023-06-03T07:48:00Z
|
Impact of translation on biomedical information extraction from
real-life clinical notes
|
[
"Christel Gérardin",
"Yuhan Xiong",
"Perceval Wajsbürt",
"Fabrice Carrat",
"Xavier Tannier"
] |
The objective of our study is to determine whether using English tools to
extract and normalize French medical concepts on translations provides
comparable performance to French models trained on a set of annotated French
clinical notes. We compare two methods: a method involving French language
models and a method involving English language models. For the native French
method, the Named Entity Recognition (NER) and normalization steps are
performed separately. For the translated English method, after the first
translation step, we compare a two-step method and a terminology-oriented
method that performs extraction and normalization at the same time. We used
French, English and bilingual annotated datasets to evaluate all steps (NER,
normalization and translation) of our algorithms. Concerning the results, the
native French method performs better than the translated English one with a
global f1 score of 0.51 [0.47;0.55] against 0.39 [0.34;0.44] and 0.38
[0.36;0.40] for the two English methods tested. In conclusion, despite the
recent improvement of the translation models, there is a significant
performance difference between the two approaches in favor of the native French
method which is more efficient on French medical texts, even with few annotated
documents.
|
[
"cs.CL"
] | false |
2306.02175
|
2023-06-03T18:38:02Z
|
TART: Improved Few-shot Text Classification Using Task-Adaptive
Reference Transformation
|
[
"Shuo Lei",
"Xuchao Zhang",
"Jianfeng He",
"Fanglan Chen",
"Chang-Tien Lu"
] |
Meta-learning has emerged as a trending technique to tackle few-shot text
classification and achieve state-of-the-art performance. However, the
performance of existing approaches heavily depends on the inter-class variance
of the support set. As a result, it can perform well on tasks when the
semantics of sampled classes are distinct while failing to differentiate
classes with similar semantics. In this paper, we propose a novel Task-Adaptive
Reference Transformation (TART) network, aiming to enhance the generalization
by transforming the class prototypes to per-class fixed reference points in
task-adaptive metric spaces. To further maximize divergence between transformed
prototypes in task-adaptive metric spaces, TART introduces a discriminative
reference regularization among transformed prototypes. Extensive experiments
are conducted on four benchmark datasets and our method demonstrates clear
superiority over the state-of-the-art models in all the datasets. In
particular, our model surpasses the state-of-the-art method by 7.4% and 5.4% in
1-shot and 5-shot classification on the 20 Newsgroups dataset, respectively.
|
[
"cs.CL"
] | false |
2306.02182
|
2023-06-03T19:38:04Z
|
FlairNLP at SemEval-2023 Task 6b: Extraction of Legal Named Entities
from Legal Texts using Contextual String Embeddings
|
[
"Vinay N Ramesh",
"Rohan Eswara"
] |
Indian court legal texts and processes are essential towards the integrity of
the judicial system and towards maintaining the social and political order of
the nation. Due to the increase in number of pending court cases, there is an
urgent need to develop tools to automate many of the legal processes with the
knowledge of artificial intelligence. In this paper, we employ knowledge
extraction techniques, specially the named entity extraction of legal entities
within court case judgements. We evaluate several state of the art
architectures in the realm of sequence labeling using models trained on a
curated dataset of legal texts. We observe that a Bi-LSTM model trained on
Flair Embeddings achieves the best results, and we also publish the BIO
formatted dataset as part of this paper.
|
[
"cs.CL"
] | false |
2306.02190
|
2023-06-03T20:12:27Z
|
Stubborn Lexical Bias in Data and Models
|
[
"Sofia Serrano",
"Jesse Dodge",
"Noah A. Smith"
] |
In NLP, recent work has seen increased focus on spurious correlations between
various features and labels in training data, and how these influence model
behavior. However, the presence and effect of such correlations are typically
examined feature by feature. We investigate the cumulative impact on a model of
many such intersecting features. Using a new statistical method, we examine
whether such spurious patterns in data appear in models trained on the data. We
select two tasks -- natural language inference and duplicate-question detection
-- for which any unigram feature on its own should ideally be uninformative,
which gives us a large pool of automatically extracted features with which to
experiment. The large size of this pool allows us to investigate the
intersection of features spuriously associated with (potentially different)
labels. We then apply an optimization approach to *reweight* the training data,
reducing thousands of spurious correlations, and examine how doing so affects
models trained on the reweighted data. Surprisingly, though this method can
successfully reduce lexical biases in the training data, we still find strong
evidence of corresponding bias in the trained models, including worsened bias
for slightly more complex features (bigrams). We close with discussion about
the implications of our results on what it means to "debias" training data, and
how issues of data quality can affect model bias.
|
[
"cs.CL"
] | false |
2306.02196
|
2023-06-03T20:59:19Z
|
Question-Context Alignment and Answer-Context Dependencies for Effective
Answer Sentence Selection
|
[
"Minh Van Nguyen",
"Kishan KC",
"Toan Nguyen",
"Thien Huu Nguyen",
"Ankit Chadha",
"Thuy Vu"
] |
Answer sentence selection (AS2) in open-domain question answering finds
answer for a question by ranking candidate sentences extracted from web
documents. Recent work exploits answer context, i.e., sentences around a
candidate, by incorporating them as additional input string to the Transformer
models to improve the correctness scoring. In this paper, we propose to improve
the candidate scoring by explicitly incorporating the dependencies between
question-context and answer-context into the final representation of a
candidate. Specifically, we use Optimal Transport to compute the question-based
dependencies among sentences in the passage where the answer is extracted from.
We then represent these dependencies as edges in a graph and use Graph
Convolutional Network to derive the representation of a candidate, a node in
the graph. Our proposed model achieves significant improvements on popular AS2
benchmarks, i.e., WikiQA and WDRASS, obtaining new state-of-the-art on all
benchmarks.
|
[
"cs.CL"
] | false |
2306.02077
|
2023-06-03T10:54:23Z
|
Utilizing ChatGPT to Enhance Clinical Trial Enrollment
|
[
"Georgios Peikos",
"Symeon Symeonidis",
"Pranav Kasela",
"Gabriella Pasi"
] |
Clinical trials are a critical component of evaluating the effectiveness of
new medical interventions and driving advancements in medical research.
Therefore, timely enrollment of patients is crucial to prevent delays or
premature termination of trials. In this context, Electronic Health Records
(EHRs) have emerged as a valuable tool for identifying and enrolling eligible
participants. In this study, we propose an automated approach that leverages
ChatGPT, a large language model, to extract patient-related information from
unstructured clinical notes and generate search queries for retrieving
potentially eligible clinical trials. Our empirical evaluation, conducted on
two benchmark retrieval collections, shows improved retrieval performance
compared to existing approaches when several general-purposed and task-specific
prompts are used. Notably, ChatGPT-generated queries also outperform
human-generated queries in terms of retrieval performance. These findings
highlight the potential use of ChatGPT to enhance clinical trial enrollment
while ensuring the quality of medical service and minimizing direct risks to
patients.
|
[
"cs.IR",
"cs.CL"
] | false |
2306.02078
|
2023-06-03T10:56:44Z
|
Incorporating Deep Syntactic and Semantic Knowledge for Chinese Sequence
Labeling with GCN
|
[
"Xuemei Tang",
"Jun Wang",
"Qi Su"
] |
Recently, it is quite common to integrate Chinese sequence labeling results
to enhance syntactic and semantic parsing. However, little attention has been
paid to the utility of hierarchy and structure information encoded in syntactic
and semantic features for Chinese sequence labeling tasks. In this paper, we
propose a novel framework to encode syntactic structure features and semantic
information for Chinese sequence labeling tasks with graph convolutional
networks (GCN). Experiments on five benchmark datasets, including Chinese word
segmentation and part-of-speech tagging, demonstrate that our model can
effectively improve the performance of Chinese labeling tasks.
|
[
"cs.CL",
"cs.AI"
] | false |
2306.02140
|
2023-06-03T15:41:59Z
|
Unsupervised Human Activity Recognition through Two-stage Prompting with
ChatGPT
|
[
"Qingxin Xia",
"Takuya Maekawa",
"Takahiro Hara"
] |
Wearable sensor devices, which offer the advantage of recording daily objects
used by a person while performing an activity, enable the feasibility of
unsupervised Human Activity Recognition (HAR). Unfortunately, previous
unsupervised approaches using the usage sequence of objects usually require a
proper description of activities manually prepared by humans. Instead, we
leverage the knowledge embedded in a Large Language Model (LLM) of ChatGPT.
Because the sequence of objects robustly characterizes the activity identity,
it is possible that ChatGPT already learned the association between activities
and objects from existing contexts. However, previous prompt engineering for
ChatGPT exhibits limited generalization ability when dealing with a list of
words (i.e., sequence of objects) due to the similar weighting assigned to each
word in the list. In this study, we propose a two-stage prompt engineering,
which first guides ChatGPT to generate activity descriptions associated with
objects while emphasizing important objects for distinguishing similar
activities; then outputs activity classes and explanations for enhancing the
contexts that are helpful for HAR. To the best of our knowledge, this is the
first study that utilizes ChatGPT to recognize activities using objects in an
unsupervised manner. We conducted our approach on three datasets and
demonstrated the state-of-the-art performance.
|
[
"cs.HC",
"cs.CL"
] | false |
2306.02193
|
2023-06-03T20:37:46Z
|
LDEB -- Label Digitization with Emotion Binarization and Machine
Learning for Emotion Recognition in Conversational Dialogues
|
[
"Amitabha Dey",
"Shan Suthaharan"
] |
Emotion recognition in conversations (ERC) is vital to the advancements of
conversational AI and its applications. Therefore, the development of an
automated ERC model using the concepts of machine learning (ML) would be
beneficial. However, the conversational dialogues present a unique problem
where each dialogue depicts nested emotions that entangle the association
between the emotional feature descriptors and emotion type (or label). This
entanglement that can be multiplied with the presence of data paucity is an
obstacle for a ML model. To overcome this problem, we proposed a novel approach
called Label Digitization with Emotion Binarization (LDEB) that disentangles
the twists by utilizing the text normalization and 7-bit digital encoding
techniques and constructs a meaningful feature space for a ML model to be
trained. We also utilized the publicly available dataset called the
FETA-DailyDialog dataset for feature learning and developed a hierarchical ERC
model using random forest (RF) and artificial neural network (ANN) classifiers.
Simulations showed that the ANN-based ERC model was able to predict emotion
with the best accuracy and precision scores of about 74% and 76%, respectively.
Simulations also showed that the ANN-model could reach a training accuracy
score of about 98% with 60 epochs. On the other hand, the RF-based ERC model
was able to predict emotions with the best accuracy and precision scores of
about 78% and 75%, respectively.
|
[
"cs.CL",
"cs.LG"
] | false |
2306.02038
|
2023-06-03T07:32:25Z
|
Span Identification of Epistemic Stance-Taking in Academic Written
English
|
[
"Masaki Eguchi",
"Kristopher Kyle"
] |
Responding to the increasing need for automated writing evaluation (AWE)
systems to assess language use beyond lexis and grammar (Burstein et al.,
2016), we introduce a new approach to identify rhetorical features of stance in
academic English writing. Drawing on the discourse-analytic framework of
engagement in the Appraisal analysis (Martin & White, 2005), we manually
annotated 4,688 sentences (126,411 tokens) for eight rhetorical stance
categories (e.g., PROCLAIM, ATTRIBUTION) and additional discourse elements. We
then report an experiment to train machine learning models to identify and
categorize the spans of these stance expressions. The best-performing model
(RoBERTa + LSTM) achieved macro-averaged F1 of .7208 in the span identification
of stance-taking expressions, slightly outperforming the intercoder reliability
estimates before adjudication (F1 = .6629).
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2306.02153
|
2023-06-03T16:44:21Z
|
Acoustic Word Embeddings for Untranscribed Target Languages with
Continued Pretraining and Learned Pooling
|
[
"Ramon Sanabria",
"Ondrej Klejch",
"Hao Tang",
"Sharon Goldwater"
] |
Acoustic word embeddings are typically created by training a pooling function
using pairs of word-like units. For unsupervised systems, these are mined using
k-nearest neighbor (KNN) search, which is slow. Recently, mean-pooled
representations from a pre-trained self-supervised English model were suggested
as a promising alternative, but their performance on target languages was not
fully competitive. Here, we explore improvements to both approaches: we use
continued pre-training to adapt the self-supervised model to the target
language, and we use a multilingual phone recognizer (MPR) to mine phone n-gram
pairs for training the pooling function. Evaluating on four languages, we show
that both methods outperform a recent approach on word discrimination.
Moreover, the MPR method is orders of magnitude faster than KNN, and is highly
data efficient. We also show a small improvement from performing learned
pooling on top of the continued pre-trained representations.
|
[
"cs.CL",
"cs.LG",
"cs.SD",
"eess.AS"
] | false |
2306.01963
|
2023-06-03T00:16:27Z
|
Over-the-Air Federated Learning In Broadband Communication
|
[
"Wayne Lemieux",
"Raphael Pinard",
"Mitra Hassani"
] |
Federated learning (FL) is a privacy-preserving distributed machine learning
paradigm that operates at the wireless edge. It enables clients to collaborate
on model training while keeping their data private from adversaries and the
central server. However, current FL approaches have limitations. Some rely on
secure multiparty computation, which can be vulnerable to inference attacks.
Others employ differential privacy, but this may lead to decreased test
accuracy when dealing with a large number of parties contributing small amounts
of data. To address these issues, this paper proposes a novel approach that
integrates federated learning seamlessly into the inner workings of MIMO
(Multiple-Input Multiple-Output) systems.
|
[
"cs.LG"
] | false |
2306.01977
|
2023-06-03T01:21:58Z
|
AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedIn
|
[
"Zhentao Xu",
"Ruoying Wang",
"Girish Balaji",
"Manas Bundele",
"Xiaofei Liu",
"Leo Liu",
"Tie Wang"
] |
Data-driven companies use AI models extensively to develop products and
intelligent business solutions, making the health of these models crucial for
business success. Model monitoring and alerting in industries pose unique
challenges, including a lack of clear model health metrics definition, label
sparsity, and fast model iterations that result in short-lived models and
features. As a product, there are also requirements for scalability,
generalizability, and explainability. To tackle these challenges, we propose
AlerTiger, a deep-learning-based MLOps model monitoring system that helps AI
teams across the company monitor their AI models' health by detecting anomalies
in models' input features and output score over time. The system consists of
four major steps: model statistics generation, deep-learning-based anomaly
detection, anomaly post-processing, and user alerting. Our solution generates
three categories of statistics to indicate AI model health, offers a two-stage
deep anomaly detection solution to address label sparsity and attain the
generalizability of monitoring new models, and provides holistic reports for
actionable alerts. This approach has been deployed to most of LinkedIn's
production AI models for over a year and has identified several model issues
that later led to significant business metric gains after fixing.
|
[
"cs.LG",
"I.2"
] | false |
2306.02006
|
2023-06-03T05:32:19Z
|
MA2CL:Masked Attentive Contrastive Learning for Multi-Agent
Reinforcement Learning
|
[
"Haolin Song",
"Mingxiao Feng",
"Wengang Zhou",
"Houqiang Li"
] |
Recent approaches have utilized self-supervised auxiliary tasks as
representation learning to improve the performance and sample efficiency of
vision-based reinforcement learning algorithms in single-agent settings.
However, in multi-agent reinforcement learning (MARL), these techniques face
challenges because each agent only receives partial observation from an
environment influenced by others, resulting in correlated observations in the
agent dimension. So it is necessary to consider agent-level information in
representation learning for MARL. In this paper, we propose an effective
framework called \textbf{M}ulti-\textbf{A}gent \textbf{M}asked
\textbf{A}ttentive \textbf{C}ontrastive \textbf{L}earning (MA2CL), which
encourages learning representation to be both temporal and agent-level
predictive by reconstructing the masked agent observation in latent space.
Specifically, we use an attention reconstruction model for recovering and the
model is trained via contrastive learning. MA2CL allows better utilization of
contextual information at the agent level, facilitating the training of MARL
agents for cooperation tasks. Extensive experiments demonstrate that our method
significantly improves the performance and sample efficiency of different MARL
algorithms and outperforms other methods in various vision-based and
state-based scenarios. Our code can be found in
\url{https://github.com/ustchlsong/MA2CL}
|
[
"cs.LG"
] | false |
2306.02025
|
2023-06-03T06:51:22Z
|
Exploring Global and Local Information for Anomaly Detection with Normal
Samples
|
[
"Fan Xu",
"Nan Wang",
"Xibin Zhao"
] |
Anomaly detection aims to detect data that do not conform to regular
patterns, and such data is also called outliers. The anomalies to be detected
are often tiny in proportion, containing crucial information, and are suitable
for application scenes like intrusion detection, fraud detection, fault
diagnosis, e-commerce platforms, et al. However, in many realistic scenarios,
only the samples following normal behavior are observed, while we can hardly
obtain any anomaly information. To address such problem, we propose an anomaly
detection method GALDetector which is combined of global and local information
based on observed normal samples. The proposed method can be divided into a
three-stage method. Firstly, the global similar normal scores and the local
sparsity scores of unlabeled samples are computed separately. Secondly,
potential anomaly samples are separated from the unlabeled samples
corresponding to these two scores and corresponding weights are assigned to the
selected samples. Finally, a weighted anomaly detector is trained by loads of
samples, then the detector is utilized to identify else anomalies. To evaluate
the effectiveness of the proposed method, we conducted experiments on three
categories of real-world datasets from diverse domains, and experimental
results show that our method achieves better performance when compared with
other state-of-the-art methods.
|
[
"cs.LG"
] | false |
2306.02161
|
2023-06-03T17:10:33Z
|
Few-Shot Open-Set Learning for On-Device Customization of KeyWord
Spotting Systems
|
[
"Manuele Rusci",
"Tinne Tuytelaars"
] |
A personalized KeyWord Spotting (KWS) pipeline typically requires the
training of a Deep Learning model on a large set of user-defined speech
utterances, preventing fast customization directly applied on-device. To fill
this gap, this paper investigates few-shot learning methods for open-set KWS
classification by combining a deep feature encoder with a prototype-based
classifier. With user-defined keywords from 10 classes of the Google Speech
Command dataset, our study reports an accuracy of up to 76% in a 10-shot
scenario while the false acceptance rate of unknown data is kept to 5%. In the
analyzed settings, the usage of the triplet loss to train an encoder with
normalized output features performs better than the prototypical networks
jointly trained with a generator of dummy unknown-class prototypes. This design
is also more effective than encoders trained on a classification problem and
features fewer parameters than other iso-accuracy approaches.
|
[
"cs.LG"
] | false |
2306.01995
|
2023-06-03T04:00:47Z
|
Asymptotically Optimal Pure Exploration for Infinite-Armed Bandits
|
[
"Xiao-Yue Gong",
"Mark Sellke"
] |
We study pure exploration with infinitely many bandit arms generated i.i.d.
from an unknown distribution. Our goal is to efficiently select a single high
quality arm whose average reward is, with probability $1-\delta$, within
$\varepsilon$ of being among the top $\eta$-fraction of arms; this is a natural
adaptation of the classical PAC guarantee for infinite action sets. We consider
both the fixed confidence and fixed budget settings, aiming respectively for
minimal expected and fixed sample complexity.
For fixed confidence, we give an algorithm with expected sample complexity
$O\left(\frac{\log (1/\eta)\log (1/\delta)}{\eta\varepsilon^2}\right)$. This is
optimal except for the $\log (1/\eta)$ factor, and the $\delta$-dependence
closes a quadratic gap in the literature. For fixed budget, we show the
asymptotically optimal sample complexity as $\delta\to 0$ is
$c^{-1}\log(1/\delta)\big(\log\log(1/\delta)\big)^2$ to leading order.
Equivalently, the optimal failure probability given exactly $N$ samples decays
as $\exp\big(-cN/\log^2 N\big)$, up to a factor $1\pm o_N(1)$ inside the
exponent. The constant $c$ depends explicitly on the problem parameters
(including the unknown arm distribution) through a certain Fisher information
distance. Even the strictly super-linear dependence on $\log(1/\delta)$ was not
known and resolves a question of Grossman and Moshkovitz (FOCS 2016, SIAM
Journal on Computing 2020).
|
[
"cs.LG",
"stat.ML"
] | false |
2306.01999
|
2023-06-03T04:23:49Z
|
GAT-GAN : A Graph-Attention-based Time-Series Generative Adversarial
Network
|
[
"Srikrishna Iyer",
"Teng Teck Hou"
] |
Generative Adversarial Networks (GANs) have proven to be a powerful tool for
generating realistic synthetic data. However, traditional GANs often struggle
to capture complex relationships between features which results in generation
of unrealistic multivariate time-series data. In this paper, we propose a
Graph-Attention-based Generative Adversarial Network (GAT-GAN) that explicitly
includes two graph-attention layers, one that learns temporal dependencies
while the other captures spatial relationships. Unlike RNN-based GANs that
struggle with modeling long sequences of data points, GAT-GAN generates long
time-series data of high fidelity using an adversarially trained autoencoder
architecture. Our empirical evaluations, using a variety of real-time-series
datasets, show that our framework consistently outperforms state-of-the-art
benchmarks based on \emph{Frechet Transformer distance} and \emph{Predictive
score}, that characterizes (\emph{Fidelity, Diversity}) and \emph{predictive
performance} respectively. Moreover, we introduce a Frechet Inception
distance-like (FID) metric for time-series data called Frechet Transformer
distance (FTD) score (lower is better), to evaluate the quality and variety of
generated data. We also found that low FTD scores correspond to the
best-performing downstream predictive experiments. Hence, FTD scores can be
used as a standardized metric to evaluate synthetic time-series data.
|
[
"cs.LG",
"cs.AI"
] | false |
2306.02150
|
2023-06-03T16:36:43Z
|
An information field theory approach to Bayesian state and parameter
estimation in dynamical systems
|
[
"Kairui Hao",
"Ilias Bilionis"
] |
Dynamical system state estimation and parameter calibration problems are
ubiquitous across science and engineering. Bayesian approaches to the problem
are the gold standard as they allow for the quantification of uncertainties and
enable the seamless fusion of different experimental modalities. When the
dynamics are discrete and stochastic, one may employ powerful techniques such
as Kalman, particle, or variational filters. Practitioners commonly apply these
methods to continuous-time, deterministic dynamical systems after discretizing
the dynamics and introducing fictitious transition probabilities. However,
approaches based on time-discretization suffer from the curse of dimensionality
since the number of random variables grows linearly with the number of
time-steps. Furthermore, the introduction of fictitious transition
probabilities is an unsatisfactory solution because it increases the number of
model parameters and may lead to inference bias. To address these drawbacks,
the objective of this paper is to develop a scalable Bayesian approach to state
and parameter estimation suitable for continuous-time, deterministic dynamical
systems. Our methodology builds upon information field theory. Specifically, we
construct a physics-informed prior probability measure on the function space of
system responses so that functions that satisfy the physics are more likely.
This prior allows us to quantify model form errors. We connect the system's
response to observations through a probabilistic model of the measurement
process. The joint posterior over the system responses and all parameters is
given by Bayes' rule. To approximate the intractable posterior, we develop a
stochastic variational inference algorithm. In summary, the developed
methodology offers a powerful framework for Bayesian estimation in dynamical
systems.
|
[
"physics.data-an",
"cs.LG"
] | false |
2306.02169
|
2023-06-03T18:22:01Z
|
Probabilistic Solar Proxy Forecasting with Neural Network Ensembles
|
[
"Joshua D. Daniell",
"Piyush M. Mehta"
] |
Space weather indices are used commonly to drive forecasts of thermosphere
density, which directly affects objects in low-Earth orbit (LEO) through
atmospheric drag. One of the most commonly used space weather proxies, $F_{10.7
cm}$, correlates well with solar extreme ultra-violet (EUV) energy deposition
into the thermosphere. Currently, the USAF contracts Space Environment
Technologies (SET), which uses a linear algorithm to forecast $F_{10.7 cm}$. In
this work, we introduce methods using neural network ensembles with multi-layer
perceptrons (MLPs) and long-short term memory (LSTMs) to improve on the SET
predictions. We make predictions only from historical $F_{10.7 cm}$ values, but
also investigate data manipulation to improve forecasting. We investigate data
manipulation methods (backwards averaging and lookback) as well as multi step
and dynamic forecasting. This work shows an improvement over the baseline when
using ensemble methods. The best models found in this work are ensemble
approaches using multi step or a combination of multi step and dynamic
predictions. Nearly all approaches offer an improvement, with the best models
improving between 45 and 55\% on relative MSE. Other relative error metrics
were shown to improve greatly when ensembles methods were used. We were also
able to leverage the ensemble approach to provide a distribution of predicted
values; allowing an investigation into forecast uncertainty. Our work found
models that produced less biased predictions at elevated and high solar
activity levels. Uncertainty was also investigated through the use of a
calibration error score metric (CES), our best ensemble reached similar CES as
other work.
|
[
"physics.space-ph",
"cs.LG"
] | false |
2306.05286
|
2023-06-03T02:45:03Z
|
JGAT: a joint spatio-temporal graph attention model for brain decoding
|
[
"Han Yi Chiu",
"Liang Zhao",
"Anqi Wu"
] |
The decoding of brain neural networks has been an intriguing topic in
neuroscience for a well-rounded understanding of different types of brain
disorders and cognitive stimuli. Integrating different types of connectivity,
e.g., Functional Connectivity (FC) and Structural Connectivity (SC), from
multi-modal imaging techniques can take their complementary information into
account and therefore have the potential to get better decoding capability.
However, traditional approaches for integrating FC and SC overlook the
dynamical variations, which stand a great chance to over-generalize the brain
neural network. In this paper, we propose a Joint kernel Graph Attention
Network (JGAT), which is a new multi-modal temporal graph attention network
framework. It integrates the data from functional Magnetic Resonance Images
(fMRI) and Diffusion Weighted Imaging (DWI) while preserving the dynamic
information at the same time. We conduct brain-decoding tasks with our JGAT on
four independent datasets: three of 7T fMRI datasets from the Human Connectome
Project (HCP) and one from animal neural recordings. Furthermore, with
Attention Scores (AS) and Frame Scores (FS) computed and learned from the
model, we can locate several informative temporal segments and build meaningful
dynamical pathways along the temporal domain for the HCP datasets. The URL to
the code of JGAT model: https://github.com/BRAINML-GT/JGAT.
|
[
"q-bio.NC",
"cs.LG"
] | false |
2306.01993
|
2023-06-03T03:42:30Z
|
Provable benefits of score matching
|
[
"Chirag Pabbaraju",
"Dhruv Rohatgi",
"Anish Sevekari",
"Holden Lee",
"Ankur Moitra",
"Andrej Risteski"
] |
Score matching is an alternative to maximum likelihood (ML) for estimating a
probability distribution parametrized up to a constant of proportionality. By
fitting the ''score'' of the distribution, it sidesteps the need to compute
this constant of proportionality (which is often intractable). While score
matching and variants thereof are popular in practice, precise theoretical
understanding of the benefits and tradeoffs with maximum likelihood -- both
computational and statistical -- are not well understood. In this work, we give
the first example of a natural exponential family of distributions such that
the score matching loss is computationally efficient to optimize, and has a
comparable statistical efficiency to ML, while the ML loss is intractable to
optimize using a gradient-based method. The family consists of exponentials of
polynomials of fixed degree, and our result can be viewed as a continuous
analogue of recent developments in the discrete setting. Precisely, we show:
(1) Designing a zeroth-order or first-order oracle for optimizing the maximum
likelihood loss is NP-hard. (2) Maximum likelihood has a statistical efficiency
polynomial in the ambient dimension and the radius of the parameters of the
family. (3) Minimizing the score matching loss is both computationally and
statistically efficient, with complexity polynomial in the ambient dimension.
|
[
"cs.LG",
"cs.DS",
"stat.ML"
] | false |
2306.02002
|
2023-06-03T04:56:04Z
|
Can Directed Graph Neural Networks be Adversarially Robust?
|
[
"Zhichao Hou",
"Xitong Zhang",
"Wei Wang",
"Charu C. Aggarwal",
"Xiaorui Liu"
] |
The existing research on robust Graph Neural Networks (GNNs) fails to
acknowledge the significance of directed graphs in providing rich information
about networks' inherent structure. This work presents the first investigation
into the robustness of GNNs in the context of directed graphs, aiming to
harness the profound trust implications offered by directed graphs to bolster
the robustness and resilience of GNNs. Our study reveals that existing directed
GNNs are not adversarially robust. In pursuit of our goal, we introduce a new
and realistic directed graph attack setting and propose an innovative,
universal, and efficient message-passing framework as a plug-in layer to
significantly enhance the robustness of GNNs. Combined with existing defense
strategies, this framework achieves outstanding clean accuracy and
state-of-the-art robust performance, offering superior defense against both
transfer and adaptive attacks. The findings in this study reveal a novel and
promising direction for this crucial research area. The code will be made
publicly available upon the acceptance of this work.
|
[
"cs.LG",
"cs.AI",
"cs.CR"
] | false |
2306.02015
|
2023-06-03T06:19:20Z
|
Machine learning enabled experimental design and parameter estimation
for ultrafast spin dynamics
|
[
"Zhantao Chen",
"Cheng Peng",
"Alexander N. Petsch",
"Sathya R. Chitturi",
"Alana Okullo",
"Sugata Chowdhury",
"Chun Hong Yoon",
"Joshua J. Turner"
] |
Advanced experimental measurements are crucial for driving theoretical
developments and unveiling novel phenomena in condensed matter and material
physics, which often suffer from the scarcity of facility resources and
increasing complexities. To address the limitations, we introduce a methodology
that combines machine learning with Bayesian optimal experimental design
(BOED), exemplified with x-ray photon fluctuation spectroscopy (XPFS)
measurements for spin fluctuations. Our method employs a neural network model
for large-scale spin dynamics simulations for precise distribution and utility
calculations in BOED. The capability of automatic differentiation from the
neural network model is further leveraged for more robust and accurate
parameter estimation. Our numerical benchmarks demonstrate the superior
performance of our method in guiding XPFS experiments, predicting model
parameters, and yielding more informative measurements within limited
experimental time. Although focusing on XPFS and spin fluctuations, our method
can be adapted to other experiments, facilitating more efficient data
collection and accelerating scientific discoveries.
|
[
"cond-mat.mtrl-sci",
"cs.LG",
"physics.comp-ph",
"physics.data-an"
] | false |
2306.02108
|
2023-06-03T13:16:17Z
|
Random matrix theory and the loss surfaces of neural networks
|
[
"Nicholas P Baskerville"
] |
Neural network models are one of the most successful approaches to machine
learning, enjoying an enormous amount of development and research over recent
years and finding concrete real-world applications in almost any conceivable
area of science, engineering and modern life in general. The theoretical
understanding of neural networks trails significantly behind their practical
success and the engineering heuristics that have grown up around them. Random
matrix theory provides a rich framework of tools with which aspects of neural
network phenomenology can be explored theoretically. In this thesis, we
establish significant extensions of prior work using random matrix theory to
understand and describe the loss surfaces of large neural networks,
particularly generalising to different architectures. Informed by the
historical applications of random matrix theory in physics and elsewhere, we
establish the presence of local random matrix universality in real neural
networks and then utilise this as a modeling assumption to derive powerful and
novel results about the Hessians of neural network loss surfaces and their
spectra. In addition to these major contributions, we make use of random matrix
models for neural network loss surfaces to shed light on modern neural network
training approaches and even to derive a novel and effective variant of a
popular optimisation algorithm.
Overall, this thesis provides important contributions to cement the place of
random matrix theory in the theoretical study of modern neural networks,
reveals some of the limits of existing approaches and begins the study of an
entirely new role for random matrix theory in the theory of deep learning with
important experimental discoveries and novel theoretical results based on local
random matrix universality.
|
[
"math-ph",
"cs.LG",
"math.MP",
"math.PR"
] | false |
2306.02149
|
2023-06-03T16:34:25Z
|
Infomorphic networks: Locally learning neural networks derived from
partial information decomposition
|
[
"Marcel Graetz",
"Abdullah Makkeh",
"Andreas C. Schneider",
"David A. Ehrlich",
"Viola Priesemann",
"Michael Wibral"
] |
Understanding the intricate cooperation among individual neurons in
performing complex tasks remains a challenge to this date. In this paper, we
propose a novel type of model neuron that emulates the functional
characteristics of biological neurons by optimizing an abstract local
information processing goal. We have previously formulated such a goal function
based on principles from partial information decomposition (PID). Here, we
present a corresponding parametric local learning rule which serves as the
foundation of "infomorphic networks" as a novel concrete model of neural
networks. We demonstrate the versatility of these networks to perform tasks
from supervised, unsupervised and memory learning. By leveraging the
explanatory power and interpretable nature of the PID framework, these
infomorphic networks represent a valuable tool to advance our understanding of
cortical function.
|
[
"cs.IT",
"cs.LG",
"cs.NE",
"math.IT"
] | false |
2306.02165
|
2023-06-03T17:51:04Z
|
Learning to Defend by Attacking (and Vice-Versa): Transfer of Learning
in Cybersecurity Games
|
[
"Tyler Malloy",
"Cleotilde Gonzalez"
] |
Designing cyber defense systems to account for cognitive biases in human
decision making has demonstrated significant success in improving performance
against human attackers. However, much of the attention in this area has
focused on relatively simple accounts of biases in human attackers, and little
is known about adversarial behavior or how defenses could be improved by
disrupting attacker's behavior. In this work, we present a novel model of human
decision-making inspired by the cognitive faculties of Instance-Based Learning
Theory, Theory of Mind, and Transfer of Learning. This model functions by
learning from both roles in a security scenario: defender and attacker, and by
making predictions of the opponent's beliefs, intentions, and actions. The
proposed model can better defend against attacks from a wide range of opponents
compared to alternatives that attempt to perform optimally without accounting
for human biases. Additionally, the proposed model performs better against a
range of human-like behavior by explicitly modeling human transfer of learning,
which has not yet been applied to cyber defense scenarios. Results from
simulation experiments demonstrate the potential usefulness of cognitively
inspired models of agents trained in attack and defense roles and how these
insights could potentially be used in real-world cybersecurity.
|
[
"cs.AI",
"cs.CR",
"cs.LG"
] | false |
2306.02174
|
2023-06-03T18:36:12Z
|
Training Data Attribution for Diffusion Models
|
[
"Zheng Dai",
"David K Gifford"
] |
Diffusion models have become increasingly popular for synthesizing
high-quality samples based on training datasets. However, given the oftentimes
enormous sizes of the training datasets, it is difficult to assess how training
data impact the samples produced by a trained diffusion model. The difficulty
of relating diffusion model inputs and outputs poses significant challenges to
model explainability and training data attribution. Here we propose a novel
solution that reveals how training data influence the output of diffusion
models through the use of ensembles. In our approach individual models in an
encoded ensemble are trained on carefully engineered splits of the overall
training data to permit the identification of influential training examples.
The resulting model ensembles enable efficient ablation of training data
influence, allowing us to assess the impact of training data on model outputs.
We demonstrate the viability of these ensembles as generative models and the
validity of our approach to assessing influence.
|
[
"stat.ML",
"cs.AI",
"cs.LG"
] | false |
2306.02192
|
2023-06-03T20:34:14Z
|
Correcting auto-differentiation in neural-ODE training
|
[
"Yewei Xu",
"Shi Chen",
"Qin Li",
"Stephen J. Wright"
] |
Does the use of auto-differentiation yield reasonable updates to deep neural
networks that represent neural ODEs? Through mathematical analysis and
numerical evidence, we find that when the neural network employs high-order
forms to approximate the underlying ODE flows (such as the Linear Multistep
Method (LMM)), brute-force computation using auto-differentiation often
produces non-converging artificial oscillations. In the case of Leapfrog, we
propose a straightforward post-processing technique that effectively eliminates
these oscillations, rectifies the gradient computation and thus respects the
updates of the underlying flow.
|
[
"cs.LG",
"cs.NA",
"math.NA"
] | false |
2306.02206
|
2023-06-03T22:30:45Z
|
Mitigating Molecular Aggregation in Drug Discovery with Predictive
Insights from Explainable AI
|
[
"Hunter Sturm",
"Jonas Teufel",
"Kaitlin A. Isfeld",
"Pascal Friederich",
"Rebecca L. Davis"
] |
As the importance of high-throughput screening (HTS) continues to grow due to
its value in early stage drug discovery and data generation for training
machine learning models, there is a growing need for robust methods for
pre-screening compounds to identify and prevent false-positive hits. Small,
colloidally aggregating molecules are one of the primary sources of
false-positive hits in high-throughput screens, making them an ideal candidate
to target for removal from libraries using predictive pre-screening tools.
However, a lack of understanding of the causes of molecular aggregation
introduces difficulty in the development of predictive tools for detecting
aggregating molecules. Herein, we present an examination of the molecular
features differentiating datasets of aggregating and non-aggregating molecules,
as well as a machine learning approach to predicting molecular aggregation. Our
method uses explainable graph neural networks and counterfactuals to reliably
predict and explain aggregation, giving additional insights and design rules
for future screening. The integration of this method in HTS approaches will
help combat false positives, providing better lead molecules more rapidly and
thus accelerating drug discovery cycles.
|
[
"q-bio.BM",
"cond-mat.soft",
"cs.LG"
] | false |
2306.02208
|
2023-06-03T22:41:44Z
|
Tight Regret Bounds for Single-pass Streaming Multi-armed Bandits
|
[
"Chen Wang"
] |
Regret minimization in streaming multi-armed bandits (MABs) has been studied
extensively in recent years. In the single-pass setting with $K$ arms and $T$
trials, a regret lower bound of $\Omega(T^{2/3})$ has been proved for any
algorithm with $o(K)$ memory (Maiti et al. [NeurIPS'21]; Agarwal at al.
[COLT'22]). On the other hand, however, the previous best regret upper bound is
still $O(K^{1/3} T^{2/3}\log^{1/3}(T))$, which is achieved by the streaming
implementation of the simple uniform exploration. The $O(K^{1/3}\log^{1/3}(T))$
gap leaves the open question of the tight regret bound in the single-pass MABs
with sublinear arm memory.
In this paper, we answer this open problem and complete the picture of regret
minimization in single-pass streaming MABs. We first improve the regret lower
bound to $\Omega(K^{1/3}T^{2/3})$ for algorithms with $o(K)$ memory, which
matches the uniform exploration regret up to a logarithm factor in $T$. We then
show that the $\log^{1/3}(T)$ factor is not necessary, and we can achieve
$O(K^{1/3}T^{2/3})$ regret by finding an $\varepsilon$-best arm and committing
to it in the rest of the trials. For regret minimization with high constant
probability, we can apply the single-memory $\varepsilon$-best arm algorithms
in Jin et al. [ICML'21] to obtain the optimal bound. Furthermore, for the
expected regret minimization, we design an algorithm with a single-arm memory
that achieves $O(K^{1/3} T^{2/3}\log(K))$ regret, and an algorithm with
$O(\log^{*}(n))$-memory with the optimal $O(K^{1/3} T^{2/3})$ regret following
the $\varepsilon$-best arm algorithm in Assadi and Wang [STOC'20].
We further tested the empirical performances of our algorithms. The
simulation results show that the proposed algorithms consistently outperform
the benchmark uniform exploration algorithm by a large margin, and on occasion,
reduce the regret by up to 70%.
|
[
"cs.LG",
"cs.DS",
"stat.ML"
] | false |
2306.02212
|
2023-06-03T23:31:27Z
|
Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth
Convex Optimization
|
[
"Ruichen Jiang",
"Aryan Mokhtari"
] |
In this paper, we propose an accelerated quasi-Newton proximal extragradient
(A-QPNE) method for solving unconstrained smooth convex optimization problems.
With access only to the gradients of the objective, we prove that our method
can achieve a convergence rate of ${O}\bigl(\min\{\frac{1}{k^2},
\frac{\sqrt{d\log k}}{k^{2.5}}\}\bigr)$, where $d$ is the problem dimension and
$k$ is the number of iterations. In particular, in the regime where $k =
{O}(d)$, our method matches the optimal rate of ${O}(\frac{1}{k^2})$ by
Nesterov's accelerated gradient (NAG). Moreover, in the the regime where $k =
\Omega(d \log d)$, it outperforms NAG and converges at a faster rate of
${O}\bigl(\frac{\sqrt{d\log k}}{k^{2.5}}\bigr)$. To the best of our knowledge,
this result is the first to demonstrate a provable gain of a quasi-Newton-type
method over NAG in the convex setting. To achieve such results, we build our
method on a recent variant of the Monteiro-Svaiter acceleration framework and
adopt an online learning perspective to update the Hessian approximation
matrices, in which we relate the convergence rate of our method to the dynamic
regret of a specific online convex optimization problem in the space of
matrices.
|
[
"math.OC",
"cs.LG",
"stat.ML"
] | false |
2306.03105
|
2023-06-03T06:06:27Z
|
Data driven localized wave solution of the Fokas-Lenells equation using
modified PINN
|
[
"Gautam Kumar Saharia",
"Sagardeep Talukdar",
"Riki Dutta",
"Sudipta Nandy"
] |
We investigate data driven localized wave solutions of the Fokas-Lenells
equation by using physics informed neural network(PINN). We improve basic PINN
by incorporating control parameters into the residual loss function. We also
add conserve quantity as another loss term to modify the PINN. Using modified
PINN we obtain the data driven bright soliton and dark soliton solutions of
Fokas-Lenells equation. Conserved quantities informed loss function achieve
more accuracy in terms of relative L2 error between predicted and exact soliton
solutions. We hope that the present investigation would be useful to study the
applications of deep learning in nonlinear optics and other branches of
nonlinear physics. Source codes are available at
https://github.com/gautamksaharia/Fokas-Lenells
|
[
"nlin.PS",
"cs.LG",
"nlin.SI"
] | false |
2306.02243
|
2023-06-04T03:06:37Z
|
Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification
|
[
"Jintao Rong",
"Hao Chen",
"Tianxiao Chen",
"Linlin Ou",
"Xinyi Yu",
"Yifan Liu"
] |
Prompt learning has become a popular approach for adapting large
vision-language models, such as CLIP, to downstream tasks. Typically, prompt
learning relies on a fixed prompt token or an input-conditional token to fit a
small amount of data under full supervision. While this paradigm can generalize
to a certain range of unseen classes, it may struggle when domain gap
increases, such as in fine-grained classification and satellite image
segmentation. To address this limitation, we propose Retrieval-enhanced Prompt
learning (RePrompt), which introduces retrieval mechanisms to cache the
knowledge representations from downstream tasks. we first construct a retrieval
database from training examples, or from external examples when available. We
then integrate this retrieval-enhanced mechanism into various stages of a
simple prompt learning baseline. By referencing similar samples in the training
set, the enhanced model is better able to adapt to new tasks with few samples.
Our extensive experiments over 15 vision datasets, including 11 downstream
tasks with few-shot setting and 4 domain generalization benchmarks, demonstrate
that RePrompt achieves considerably improved performance. Our proposed approach
provides a promising solution to the challenges faced by prompt learning when
domain gap increases. The code and models will be available.
|
[
"cs.CV"
] | false |
2306.02275
|
2023-06-04T06:42:09Z
|
USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and
Segment Anything Model
|
[
"Yulin He",
"Wei Chen",
"Yusong Tan",
"Siqi Wang"
] |
Open World Object Detection (OWOD) is a novel and challenging computer vision
task that enables object detection with the ability to detect unknown objects.
Existing methods typically estimate the object likelihood with an additional
objectness branch, but ignore the conflict in learning objectness and
classification boundaries, which oppose each other on the semantic manifold and
training objective. To address this issue, we propose a simple yet effective
learning strategy, namely Decoupled Objectness Learning (DOL), which divides
the learning of these two boundaries into suitable decoder layers. Moreover,
detecting unknown objects comprehensively requires a large amount of
annotations, but labeling all unknown objects is both difficult and expensive.
Therefore, we propose to take advantage of the recent Large Vision Model (LVM),
specifically the Segment Anything Model (SAM), to enhance the detection of
unknown objects. Nevertheless, the output results of SAM contain noise,
including backgrounds and fragments, so we introduce an Auxiliary Supervision
Framework (ASF) that uses a pseudo-labeling and a soft-weighting strategies to
alleviate the negative impact of noise. Extensive experiments on popular
benchmarks, including Pascal VOC and MS COCO, demonstrate the effectiveness of
our approach. Our proposed Unknown Sensitive Detector (USD) outperforms the
recent state-of-the-art methods in terms of Unknown Recall, achieving
significant improvements of 14.3\%, 15.5\%, and 8.9\% on the M-OWODB, and
27.1\%, 29.1\%, and 25.1\% on the S-OWODB.
|
[
"cs.CV"
] | false |
2306.02277
|
2023-06-04T06:49:44Z
|
EfficientSRFace: An Efficient Network with Super-Resolution Enhancement
for Accurate Face Detection
|
[
"Guangtao Wang",
"Jun Li",
"Jie Xie",
"Jianhua Xu",
"Bo Yang"
] |
In face detection, low-resolution faces, such as numerous small faces of a
human group in a crowded scene, are common in dense face prediction tasks. They
usually contain limited visual clues and make small faces less distinguishable
from the other small objects, which poses great challenge to accurate face
detection. Although deep convolutional neural network has significantly
promoted the research on face detection recently, current deep face detectors
rarely take into account low-resolution faces and are still vulnerable to the
real-world scenarios where massive amount of low-resolution faces exist.
Consequently, they usually achieve degraded performance for low-resolution face
detection. In order to alleviate this problem, we develop an efficient detector
termed EfficientSRFace by introducing a feature-level super-resolution
reconstruction network for enhancing the feature representation capability of
the model. This module plays an auxiliary role in the training process, and can
be removed during the inference without increasing the inference time.
Extensive experiments on public benchmarking datasets, such as FDDB and WIDER
Face, show that the embedded image super-resolution module can significantly
improve the detection accuracy at the cost of a small amount of additional
parameters and computational overhead, while helping our model achieve
competitive performance compared with the state-of-the-arts methods.
|
[
"cs.CV"
] | false |
2306.02301
|
2023-06-04T08:53:28Z
|
rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for
Remote Physiological Measurement
|
[
"Xin Liu",
"Yuting Zhang",
"Zitong Yu",
"Hao Lu",
"Huanjing Yue",
"Jingyu Yang"
] |
Remote photoplethysmography (rPPG) is an important technique for perceiving
human vital signs, which has received extensive attention. For a long time,
researchers have focused on supervised methods that rely on large amounts of
labeled data. These methods are limited by the requirement for large amounts of
data and the difficulty of acquiring ground truth physiological signals. To
address these issues, several self-supervised methods based on contrastive
learning have been proposed. However, they focus on the contrastive learning
between samples, which neglect the inherent self-similar prior in physiological
signals and seem to have a limited ability to cope with noisy. In this paper, a
linear self-supervised reconstruction task was designed for extracting the
inherent self-similar prior in physiological signals. Besides, a specific
noise-insensitive strategy was explored for reducing the interference of motion
and illumination. The proposed framework in this paper, namely rPPG-MAE,
demonstrates excellent performance even on the challenging VIPL-HR dataset. We
also evaluate the proposed method on two public datasets, namely PURE and
UBFC-rPPG. The results show that our method not only outperforms existing
self-supervised methods but also exceeds the state-of-the-art (SOTA) supervised
methods. One important observation is that the quality of the dataset seems
more important than the size in self-supervised pre-training of rPPG. The
source code is released at https://github.com/linuxsino/rPPG-MAE.
|
[
"cs.CV"
] | false |
2306.02314
|
2023-06-04T09:40:25Z
|
Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation
|
[
"Haochen Wang",
"Yuchao Wang",
"Yujun Shen",
"Junsong Fan",
"Yuxi Wang",
"Zhaoxiang Zhang"
] |
The crux of label-efficient semantic segmentation is to produce high-quality
pseudo-labels to leverage a large amount of unlabeled or weakly labeled data. A
common practice is to select the highly confident predictions as the
pseudo-ground-truths for each pixel, but it leads to a problem that most pixels
may be left unused due to their unreliability. However, we argue that every
pixel matters to the model training, even those unreliable and ambiguous
pixels. Intuitively, an unreliable prediction may get confused among the top
classes, however, it should be confident about the pixel not belonging to the
remaining classes. Hence, such a pixel can be convincingly treated as a
negative key to those most unlikely categories. Therefore, we develop an
effective pipeline to make sufficient use of unlabeled data. Concretely, we
separate reliable and unreliable pixels via the entropy of predictions, push
each unreliable pixel to a category-wise queue that consists of negative keys,
and manage to train the model with all candidate pixels. Considering the
training evolution, we adaptively adjust the threshold for the
reliable-unreliable partition. Experimental results on various benchmarks and
training settings demonstrate the superiority of our approach over the
state-of-the-art alternatives.
|
[
"cs.CV"
] | false |
2306.02329
|
2023-06-04T11:08:53Z
|
Multi-CLIP: Contrastive Vision-Language Pre-training for Question
Answering tasks in 3D Scenes
|
[
"Alexandros Delitzas",
"Maria Parelli",
"Nikolas Hars",
"Georgios Vlassis",
"Sotirios Anagnostidis",
"Gregor Bachmann",
"Thomas Hofmann"
] |
Training models to apply common-sense linguistic knowledge and visual
concepts from 2D images to 3D scene understanding is a promising direction that
researchers have only recently started to explore. However, it still remains
understudied whether 2D distilled knowledge can provide useful representations
for downstream 3D vision-language tasks such as 3D question answering. In this
paper, we propose a novel 3D pre-training Vision-Language method, namely
Multi-CLIP, that enables a model to learn language-grounded and transferable 3D
scene point cloud representations. We leverage the representational power of
the CLIP model by maximizing the agreement between the encoded 3D scene
features and the corresponding 2D multi-view image and text embeddings in the
CLIP space via a contrastive objective. To validate our approach, we consider
the challenging downstream tasks of 3D Visual Question Answering (3D-VQA) and
3D Situated Question Answering (3D-SQA). To this end, we develop novel
multi-modal transformer-based architectures and we demonstrate how our
pre-training method can benefit their performance. Quantitative and qualitative
experimental results show that Multi-CLIP outperforms state-of-the-art works
across the downstream tasks of 3D-VQA and 3D-SQA and leads to a well-structured
3D scene feature space.
|
[
"cs.CV"
] | false |
2306.02346
|
2023-06-04T12:42:45Z
|
CDLT: A Dataset with Concept Drift and Long-Tailed Distribution for
Fine-Grained Visual Categorization
|
[
"Shuo Ye",
"Yufeng Shi",
"Ruxin Wang",
"Yu Wang",
"Jiamiao Xu",
"Chuanwu Yang",
"Xinge You"
] |
Data is the foundation for the development of computer vision, and the
establishment of datasets plays an important role in advancing the techniques
of fine-grained visual categorization~(FGVC). In the existing FGVC datasets
used in computer vision, it is generally assumed that each collected instance
has fixed characteristics and the distribution of different categories is
relatively balanced. In contrast, the real world scenario reveals the fact that
the characteristics of instances tend to vary with time and exhibit a
long-tailed distribution. Hence, the collected datasets may mislead the
optimization of the fine-grained classifiers, resulting in unpleasant
performance in real applications. Starting from the real-world conditions and
to promote the practical progress of fine-grained visual categorization, we
present a Concept Drift and Long-Tailed Distribution dataset. Specifically, the
dataset is collected by gathering 11195 images of 250 instances in different
species for 47 consecutive months in their natural contexts. The collection
process involves dozens of crowd workers for photographing and domain experts
for labelling. Extensive baseline experiments using the state-of-the-art
fine-grained classification models demonstrate the issues of concept drift and
long-tailed distribution existed in the dataset, which require the attention of
future researches.
|
[
"cs.CV"
] | false |
2306.02351
|
2023-06-04T13:01:19Z
|
RSSOD-Bench: A large-scale benchmark dataset for Salient Object
Detection in Optical Remote Sensing Imagery
|
[
"Zhitong Xiong",
"Yanfeng Liu",
"Qi Wang",
"Xiao Xiang Zhu"
] |
We present the RSSOD-Bench dataset for salient object detection (SOD) in
optical remote sensing imagery. While SOD has achieved success in natural scene
images with deep learning, research in SOD for remote sensing imagery (RSSOD)
is still in its early stages. Existing RSSOD datasets have limitations in terms
of scale, and scene categories, which make them misaligned with real-world
applications. To address these shortcomings, we construct the RSSOD-Bench
dataset, which contains images from four different cities in the USA. The
dataset provides annotations for various salient object categories, such as
buildings, lakes, rivers, highways, bridges, aircraft, ships, athletic fields,
and more. The salient objects in RSSOD-Bench exhibit large-scale variations,
cluttered backgrounds, and different seasons. Unlike existing datasets,
RSSOD-Bench offers uniform distribution across scene categories. We benchmark
23 different state-of-the-art approaches from both the computer vision and
remote sensing communities. Experimental results demonstrate that more research
efforts are required for the RSSOD task.
|
[
"cs.CV"
] | false |
2306.02374
|
2023-06-04T15:14:20Z
|
GAN-based Deidentification of Drivers' Face Videos: An Assessment of
Human Factors Implications in NDS Data
|
[
"Surendrabikram Thapa",
"Abhijit Sarkar"
] |
This paper addresses the problem of sharing drivers' face videos for
transportation research while adhering to proper ethical guidelines. The paper
first gives an overview of the multitude of problems associated with sharing
such data and then proposes a framework on how artificial intelligence-based
techniques, specifically face swapping, can be used for de-identifying drivers'
faces. Through extensive experimentation with an Oak Ridge National Laboratory
(ORNL) dataset, we demonstrate the effectiveness of face-swapping algorithms in
preserving essential attributes related to human factors research, including
eye movements, head movements, and mouth movements. The efficacy of the
framework was also tested on various naturalistic driving study data collected
at the Virginia Tech Transportation Institute. The results achieved through the
proposed techniques were evaluated qualitatively and quantitatively using
various metrics. Finally, we discuss possible measures for sharing the
de-identified videos with the greater research community.
|
[
"cs.CV"
] | false |
2306.02443
|
2023-06-04T19:14:44Z
|
ESTISR: Adapting Efficient Scene Text Image Super-resolution for
Real-Scenes
|
[
"Minghao Fu",
"Xin Man",
"Yihan Xu",
"Jie Shao"
] |
While scene text image super-resolution (STISR) has yielded remarkable
improvements in accurately recognizing scene text, prior methodologies have
placed excessive emphasis on optimizing performance, rather than paying due
attention to efficiency - a crucial factor in ensuring deployment of the
STISR-STR pipeline. In this work, we propose a novel Efficient Scene Text Image
Super-resolution (ESTISR) Network for resource-limited deployment platform.
ESTISR's functionality primarily depends on two critical components: a
CNN-based feature extractor and an efficient self-attention mechanism used for
decoding low-resolution images. We designed a re-parameterized inverted
residual block specifically suited for resource-limited circumstances as the
feature extractor. Meanwhile, we proposed a novel self-attention mechanism,
softmax shrinking, based on a kernel-based approach. This innovative technique
offers linear complexity while also naturally incorporating discriminating
low-level features into the self-attention structure. Extensive experiments on
TextZoom show that ESTISR retains a high image restoration quality and improved
STR accuracy of low-resolution images. Furthermore, ESTISR consistently
outperforms current methods in terms of actual running time and peak memory
consumption, while achieving a better trade-off between performance and
efficiency.
|
[
"cs.CV"
] | false |
2306.02507
|
2023-06-04T23:56:53Z
|
Deep learning powered real-time identification of insects using citizen
science data
|
[
"Shivani Chiranjeevi",
"Mojdeh Sadaati",
"Zi K Deng",
"Jayanth Koushik",
"Talukder Z Jubery",
"Daren Mueller",
"Matthew E O Neal",
"Nirav Merchant",
"Aarti Singh",
"Asheesh K Singh",
"Soumik Sarkar",
"Arti Singh",
"Baskar Ganapathysubramanian"
] |
Insect-pests significantly impact global agricultural productivity and
quality. Effective management involves identifying the full insect community,
including beneficial insects and harmful pests, to develop and implement
integrated pest management strategies. Automated identification of insects
under real-world conditions presents several challenges, including
differentiating similar-looking species, intra-species dissimilarity and
inter-species similarity, several life cycle stages, camouflage, diverse
imaging conditions, and variability in insect orientation. A deep-learning
model, InsectNet, is proposed to address these challenges. InsectNet is endowed
with five key features: (a) utilization of a large dataset of insect images
collected through citizen science; (b) label-free self-supervised learning for
large models; (c) improving prediction accuracy for species with a small sample
size; (d) enhancing model trustworthiness; and (e) democratizing access through
streamlined MLOps. This approach allows accurate identification (>96% accuracy)
of over 2500 insect species, including pollinator (e.g., butterflies, bees),
parasitoid (e.g., some wasps and flies), predator species (e.g., lady beetles,
mantises, dragonflies) and harmful pest species (e.g., armyworms, cutworms,
grasshoppers, stink bugs). InsectNet can identify invasive species, provide
fine-grained insect species identification, and work effectively in challenging
backgrounds. It also can abstain from making predictions when uncertain,
facilitating seamless human intervention and making it a practical and
trustworthy tool. InsectNet can guide citizen science data collection,
especially for invasive species where early detection is crucial. Similar
approaches may transform other agricultural challenges like disease detection
and underscore the importance of data collection, particularly through citizen
science efforts..
|
[
"cs.CV"
] | false |
2306.02263
|
2023-06-04T05:00:12Z
|
MAVD: The First Open Large-Scale Mandarin Audio-Visual Dataset with
Depth Information
|
[
"Jianrong Wang",
"Yuchen Huo",
"Li Liu",
"Tianyi Xu",
"Qi Li",
"Sen Li"
] |
Audio-visual speech recognition (AVSR) gains increasing attention from
researchers as an important part of human-computer interaction. However, the
existing available Mandarin audio-visual datasets are limited and lack the
depth information. To address this issue, this work establishes the MAVD, a new
large-scale Mandarin multimodal corpus comprising 12,484 utterances spoken by
64 native Chinese speakers. To ensure the dataset covers diverse real-world
scenarios, a pipeline for cleaning and filtering the raw text material has been
developed to create a well-balanced reading material. In particular, the latest
data acquisition device of Microsoft, Azure Kinect is used to capture depth
information in addition to the traditional audio signals and RGB images during
data acquisition. We also provide a baseline experiment, which could be used to
evaluate the effectiveness of the dataset. The dataset and code will be
released at https://github.com/SpringHuo/MAVD.
|
[
"cs.SD",
"cs.CV"
] | false |
2306.02335
|
2023-06-04T11:52:59Z
|
Towards Robust Feature Learning with t-vFM Similarity for Continual
Learning
|
[
"Bilan Gao",
"YoungBin Kim"
] |
Continual learning has been developed using standard supervised contrastive
loss from the perspective of feature learning. Due to the data imbalance during
the training, there are still challenges in learning better representations. In
this work, we suggest using a different similarity metric instead of cosine
similarity in supervised contrastive loss in order to learn more robust
representations. We validate the our method on one of the image classification
datasets Seq-CIFAR-10 and the results outperform recent continual learning
baselines.
|
[
"cs.LG",
"cs.CV"
] | false |
2306.02393
|
2023-06-04T16:05:26Z
|
Accessible Robot Control in Mixed Reality
|
[
"Ganlin Zhang",
"Deheng Zhang",
"Longteng Duan",
"Guo Han"
] |
A novel method to control the Spot robot of Boston Dynamics by Hololens 2 is
proposed. This method is mainly designed for people with physical disabilities,
users can control the robot's movement and robot arm without using their hands.
The eye gaze tracking and head motion tracking technologies of Hololens 2 are
utilized for sending control commands. The movement of the robot would follow
the eye gaze and the robot arm would mimic the pose of the user's head. Through
our experiment, our method is comparable with the traditional control method by
joystick in both time efficiency and user experience. Demo can be found on our
project webpage: https://zhangganlin.github.io/Holo-Spot-Page/index.html
|
[
"cs.RO",
"cs.CV"
] | false |
2306.02398
|
2023-06-04T16:17:19Z
|
Scale Guided Hypernetwork for Blind Super-Resolution Image Quality
Assessment
|
[
"Jun Fu"
] |
With the emergence of image super-resolution (SR) algorithm, how to blindly
evaluate the quality of super-resolution images has become an urgent task.
However, existing blind SR image quality assessment (IQA) metrics merely focus
on visual characteristics of super-resolution images, ignoring the available
scale information. In this paper, we reveal that the scale factor has a
statistically significant impact on subjective quality scores of SR images,
indicating that the scale information can be used to guide the task of blind SR
IQA. Motivated by this, we propose a scale guided hypernetwork framework that
evaluates SR image quality in a scale-adaptive manner. Specifically, the blind
SR IQA procedure is divided into three stages, i.e., content perception,
evaluation rule generation, and quality prediction. After content perception, a
hypernetwork generates the evaluation rule used in quality prediction based on
the scale factor of the SR image. We apply the proposed scale guided
hypernetwork framework to existing representative blind IQA metrics, and
experimental results show that the proposed framework not only boosts the
performance of these IQA metrics but also enhances their generalization
abilities. Source code will be available at https://github.com/JunFu1995/SGH.
|
[
"cs.CV",
"eess.IV"
] | false |
2306.02424
|
2023-06-04T17:57:51Z
|
Sanity Checks for Saliency Methods Explaining Object Detectors
|
[
"Deepan Chakravarthi Padmanabhan",
"Paul G. Plöger",
"Octavio Arriaga",
"Matias Valdenegro-Toro"
] |
Saliency methods are frequently used to explain Deep Neural Network-based
models. Adebayo et al.'s work on evaluating saliency methods for classification
models illustrate certain explanation methods fail the model and data
randomization tests. However, on extending the tests for various state of the
art object detectors we illustrate that the ability to explain a model is more
dependent on the model itself than the explanation method. We perform sanity
checks for object detection and define new qualitative criteria to evaluate the
saliency explanations, both for object classification and bounding box
decisions, using Guided Backpropagation, Integrated Gradients, and their
Smoothgrad versions, together with Faster R-CNN, SSD, and EfficientDet-D0,
trained on COCO. In addition, the sensitivity of the explanation method to
model parameters and data labels varies class-wise motivating to perform the
sanity checks for each class. We find that EfficientDet-D0 is the most
interpretable method independent of the saliency method, which passes the
sanity checks with little problems.
|
[
"cs.CV",
"cs.LG"
] | false |
2306.04466
|
2023-06-04T10:30:28Z
|
Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal
Auto-Encoder
|
[
"Tengjiao He",
"Wenguang Wang"
] |
Video anomaly detection has great potential in enhancing safety in the
production and monitoring of crucial areas. Currently, most video anomaly
detection methods are based on RGB modality, but its redundant semantic
information may breach the privacy of residents or patients. The 3D data
obtained by depth camera and LiDAR can accurately locate anomalous events in 3D
space while preserving human posture and motion information. Identifying
individuals through the point cloud is difficult due to its sparsity, which
protects personal privacy. In this study, we propose Point Spatio-Temporal
Auto-Encoder (PSTAE), an autoencoder framework that uses point cloud videos as
input to detect anomalies in point cloud videos. We introduce PSTOp and
PSTTransOp to maintain spatial geometric and temporal motion information in
point cloud videos. To measure the reconstruction loss of the proposed
autoencoder framework, we propose a reconstruction loss measurement strategy
based on a shallow feature extractor. Experimental results on the TIMo dataset
show that our method outperforms currently representative depth modality-based
methods in terms of AUROC and has superior performance in detecting Medical
Issue anomalies. These results suggest the potential of point cloud modality in
video anomaly detection. Our method sets a new state-of-the-art (SOTA) on the
TIMo dataset.
|
[
"cs.CV",
"eess.SP"
] | false |
2306.02236
|
2023-06-04T02:33:12Z
|
Detector Guidance for Multi-Object Text-to-Image Generation
|
[
"Luping Liu",
"Zijian Zhang",
"Yi Ren",
"Rongjie Huang",
"Xiang Yin",
"Zhou Zhao"
] |
Diffusion models have demonstrated impressive performance in text-to-image
generation. They utilize a text encoder and cross-attention blocks to infuse
textual information into images at a pixel level. However, their capability to
generate images with text containing multiple objects is still restricted.
Previous works identify the problem of information mixing in the CLIP text
encoder and introduce the T5 text encoder or incorporate strong prior knowledge
to assist with the alignment. We find that mixing problems also occur on the
image side and in the cross-attention blocks. The noisy images can cause
different objects to appear similar, and the cross-attention blocks inject
information at a pixel level, leading to leakage of global object understanding
and resulting in object mixing. In this paper, we introduce Detector Guidance
(DG), which integrates a latent object detection model to separate different
objects during the generation process. DG first performs latent object
detection on cross-attention maps (CAMs) to obtain object information. Based on
this information, DG then masks conflicting prompts and enhances related
prompts by manipulating the following CAMs. We evaluate the effectiveness of DG
using Stable Diffusion on COCO, CC, and a novel multi-related object benchmark,
MRO. Human evaluations demonstrate that DG provides an 8-22\% advantage in
preventing the amalgamation of conflicting concepts and ensuring that each
object possesses its unique region without any human involvement and additional
iterations. Our implementation is available at
\url{https://github.com/luping-liu/Detector-Guidance}.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2306.02268
|
2023-06-04T06:01:53Z
|
Revisiting Class Imbalance for End-to-end Semi-Supervised Object
Detection
|
[
"Purbayan Kar",
"Vishal Chudasama",
"Naoyuki Onoe",
"Pankaj Wasnik"
] |
Semi-supervised object detection (SSOD) has made significant progress with
the development of pseudo-label-based end-to-end methods. However, many of
these methods face challenges due to class imbalance, which hinders the
effectiveness of the pseudo-label generator. Furthermore, in the literature, it
has been observed that low-quality pseudo-labels severely limit the performance
of SSOD. In this paper, we examine the root causes of low-quality pseudo-labels
and present novel learning mechanisms to improve the label generation quality.
To cope with high false-negative and low precision rates, we introduce an
adaptive thresholding mechanism that helps the proposed network to filter out
optimal bounding boxes. We further introduce a Jitter-Bagging module to provide
accurate information on localization to help refine the bounding boxes.
Additionally, two new losses are introduced using the background and foreground
scores predicted by the teacher and student networks to improvise the
pseudo-label recall rate. Furthermore, our method applies strict supervision to
the teacher network by feeding strong & weak augmented data to generate robust
pseudo-labels so that it can detect small and complex objects. Finally, the
extensive experiments show that the proposed network outperforms
state-of-the-art methods on MS-COCO and Pascal VOC datasets and allows the
baseline network to achieve 100% supervised performance with much less (i.e.,
20%) labeled data.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2306.02306
|
2023-06-04T09:03:05Z
|
Cross-CBAM: A Lightweight network for Scene Segmentation
|
[
"Zhengbin Zhang",
"Zhenhao Xu",
"Xingsheng Gu",
"Juan Xiong"
] |
Scene parsing is a great challenge for real-time semantic segmentation.
Although traditional semantic segmentation networks have made remarkable
leap-forwards in semantic accuracy, the performance of inference speed is
unsatisfactory. Meanwhile, this progress is achieved with fairly large networks
and powerful computational resources. However, it is difficult to run extremely
large models on edge computing devices with limited computing power, which
poses a huge challenge to the real-time semantic segmentation tasks. In this
paper, we present the Cross-CBAM network, a novel lightweight network for
real-time semantic segmentation. Specifically, a Squeeze-and-Excitation Atrous
Spatial Pyramid Pooling Module(SE-ASPP) is proposed to get variable
field-of-view and multiscale information. And we propose a Cross Convolutional
Block Attention Module(CCBAM), in which a cross-multiply operation is employed
in the CCBAM module to make high-level semantic information guide low-level
detail information. Different from previous work, these works use attention to
focus on the desired information in the backbone. CCBAM uses cross-attention
for feature fusion in the FPN structure. Extensive experiments on the
Cityscapes dataset and Camvid dataset demonstrate the effectiveness of the
proposed Cross-CBAM model by achieving a promising trade-off between
segmentation accuracy and inference speed. On the Cityscapes test set, we
achieve 73.4% mIoU with a speed of 240.9FPS and 77.2% mIoU with a speed of
88.6FPS on NVIDIA GTX 1080Ti.
|
[
"cs.CV",
"cs.LG",
"eess.IV"
] | false |
2306.02407
|
2023-06-04T16:55:38Z
|
Heteroskedastic Geospatial Tracking with Distributed Camera Networks
|
[
"Colin Samplawski",
"Shiwei Fang",
"Ziqi Wang",
"Deepak Ganesan",
"Mani Srivastava",
"Benjamin M. Marlin"
] |
Visual object tracking has seen significant progress in recent years.
However, the vast majority of this work focuses on tracking objects within the
image plane of a single camera and ignores the uncertainty associated with
predicted object locations. In this work, we focus on the geospatial object
tracking problem using data from a distributed camera network. The goal is to
predict an object's track in geospatial coordinates along with uncertainty over
the object's location while respecting communication constraints that prohibit
centralizing raw image data. We present a novel single-object geospatial
tracking data set that includes high-accuracy ground truth object locations and
video data from a network of four cameras. We present a modeling framework for
addressing this task including a novel backbone model and explore how
uncertainty calibration and fine-tuning through a differentiable tracker affect
performance.
|
[
"cs.CV",
"cs.AI",
"cs.DC",
"cs.LG"
] | false |
2306.02459
|
2023-06-04T20:22:14Z
|
Multi-Predict: Few Shot Predictors For Efficient Neural Architecture
Search
|
[
"Yash Akhauri",
"Mohamed S. Abdelfattah"
] |
Many hardware-aware neural architecture search (NAS) methods have been
developed to optimize the topology of neural networks (NN) with the joint
objectives of higher accuracy and lower latency. Recently, both accuracy and
latency predictors have been used in NAS with great success, achieving high
sample efficiency and accurate modeling of hardware (HW) device latency
respectively. However, a new accuracy predictor needs to be trained for every
new NAS search space or NN task, and a new latency predictor needs to be
additionally trained for every new HW device. In this paper, we explore methods
to enable multi-task, multi-search-space, and multi-HW adaptation of accuracy
and latency predictors to reduce the cost of NAS. We introduce a novel
search-space independent NN encoding based on zero-cost proxies that achieves
sample-efficient prediction on multiple tasks and NAS search spaces, improving
the end-to-end sample efficiency of latency and accuracy predictors by over an
order of magnitude in multiple scenarios. For example, our NN encoding enables
multi-search-space transfer of latency predictors from NASBench-201 to FBNet
(and vice-versa) in under 85 HW measurements, a 400$\times$ improvement in
sample efficiency compared to a recent meta-learning approach. Our method also
improves the total sample efficiency of accuracy predictors by over an order of
magnitude. Finally, we demonstrate the effectiveness of our method for
multi-search-space and multi-task accuracy prediction on 28 NAS search spaces
and tasks.
|
[
"cs.LG",
"cs.AR",
"cs.CV",
"cs.PF"
] | false |
2306.02487
|
2023-06-04T21:40:11Z
|
Discussion Paper: The Threat of Real Time Deepfakes
|
[
"Guy Frankovits",
"Yisroel Mirsky"
] |
Generative deep learning models are able to create realistic audio and video.
This technology has been used to impersonate the faces and voices of
individuals. These ``deepfakes'' are being used to spread misinformation,
enable scams, perform fraud, and blackmail the innocent. The technology
continues to advance and today attackers have the ability to generate deepfakes
in real-time. This new capability poses a significant threat to society as
attackers begin to exploit the technology in advances social engineering
attacks. In this paper, we discuss the implications of this emerging threat,
identify the challenges with preventing these attacks and suggest a better
direction for researching stronger defences.
|
[
"cs.AI",
"cs.CR",
"cs.CV"
] | false |
2306.02258
|
2023-06-04T04:24:43Z
|
Probing Physical Reasoning with Counter-Commonsense Context
|
[
"Kazushi Kondo",
"Saku Sugawara",
"Akiko Aizawa"
] |
In this study, we create a CConS (Counter-commonsense Contextual Size
comparison) dataset to investigate how physical commonsense affects the
contextualized size comparison task; the proposed dataset consists of both
contexts that fit physical commonsense and those that do not. This dataset
tests the ability of language models to predict the size relationship between
objects under various contexts generated from our curated noun list and
templates. We measure the ability of several masked language models and
generative models. The results show that while large language models can use
prepositions such as ``in'' and ``into'' in the provided context to infer size
relationships, they fail to use verbs and thus make incorrect judgments led by
their prior physical commonsense.
|
[
"cs.CL"
] | false |
2306.02302
|
2023-06-04T08:54:32Z
|
Does Character-level Information Always Improve DRS-based Semantic
Parsing?
|
[
"Tomoya Kurosawa",
"Hitomi Yanaka"
] |
Even in the era of massive language models, it has been suggested that
character-level representations improve the performance of neural models. The
state-of-the-art neural semantic parser for Discourse Representation Structures
uses character-level representations, improving performance in the four
languages (i.e., English, German, Dutch, and Italian) in the Parallel Meaning
Bank dataset. However, how and why character-level information improves the
parser's performance remains unclear. This study provides an in-depth analysis
of performance changes by order of character sequences. In the experiments, we
compare F1-scores by shuffling the order and randomizing character sequences
after testing the performance of character-level information. Our results
indicate that incorporating character-level information does not improve the
performance in English and German. In addition, we find that the parser is not
sensitive to correct character order in Dutch. Nevertheless, performance
improvements are observed when using character-level information.
|
[
"cs.CL"
] | false |
2306.02334
|
2023-06-04T11:52:36Z
|
Long Text Generation Challenge
|
[
"Nikolay Mikhaylovskiy"
] |
We propose a shared task of human-like long text generation, LTG Challenge,
that asks models to output a consistent human-like long text (a Harry Potter
generic audience fanfic in English), given a prompt of about 1000 tokens. We
suggest a novel statistical metric of the text structuredness, GloVe
Autocorrelations Power/ Exponential Law Mean Absolute Percentage Error Ratio
(GAPELMAPER) and a human evaluation protocol. We hope that LTG can open new
avenues for researchers to investigate sampling approaches, prompting
strategies, autoregressive and non-autoregressive text generation architectures
and break the barrier to generate consistent long (40K+ token) texts.
|
[
"cs.CL",
"I.2.7"
] | false |
2306.02348
|
2023-06-04T12:53:12Z
|
Leverage Points in Modality Shifts: Comparing Language-only and
Multimodal Word Representations
|
[
"Aleksey Tikhonov",
"Lisa Bylinina",
"Denis Paperno"
] |
Multimodal embeddings aim to enrich the semantic information in neural
representations of language compared to text-only models. While different
embeddings exhibit different applicability and performance on downstream tasks,
little is known about the systematic representation differences attributed to
the visual modality. Our paper compares word embeddings from three
vision-and-language models (CLIP, OpenCLIP and Multilingual CLIP) and three
text-only models, with static (FastText) as well as contextual representations
(multilingual BERT; XLM-RoBERTa). This is the first large-scale study of the
effect of visual grounding on language representations, including 46 semantic
parameters. We identify meaning properties and relations that characterize
words whose embeddings are most affected by the inclusion of visual modality in
the training data; that is, points where visual grounding turns out most
important. We find that the effect of visual modality correlates most with
denotational semantic properties related to concreteness, but is also detected
for several specific semantic classes, as well as for valence, a
sentiment-related connotational property of linguistic expressions.
|
[
"cs.CL"
] | false |
2306.02405
|
2023-06-04T16:52:11Z
|
An Information-Theoretic Analysis of Self-supervised Discrete
Representations of Speech
|
[
"Badr M. Abdullah",
"Mohammed Maqsood Shaik",
"Bernd Möbius",
"Dietrich Klakow"
] |
Self-supervised representation learning for speech often involves a
quantization step that transforms the acoustic input into discrete units.
However, it remains unclear how to characterize the relationship between these
discrete units and abstract phonetic categories such as phonemes. In this
paper, we develop an information-theoretic framework whereby we represent each
phonetic category as a distribution over discrete units. We then apply our
framework to two different self-supervised models (namely wav2vec 2.0 and XLSR)
and use American English speech as a case study. Our study demonstrates that
the entropy of phonetic distributions reflects the variability of the
underlying speech sounds, with phonetically similar sounds exhibiting similar
distributions. While our study confirms the lack of direct, one-to-one
correspondence, we find an intriguing, indirect relationship between phonetic
categories and discrete units.
|
[
"cs.CL"
] | false |
2306.02408
|
2023-06-04T17:02:59Z
|
Evaluating and Improving Tool-Augmented Computation-Intensive Math
Reasoning
|
[
"Beichen Zhang",
"Kun Zhou",
"Xilin Wei",
"Wayne Xin Zhao",
"Jing Sha",
"Shijin Wang",
"Ji-Rong Wen"
] |
Chain-of-thought prompting~(CoT) and tool augmentation have been validated in
recent work as effective practices for improving large language models~(LLMs)
to perform step-by-step reasoning on complex math-related tasks. However, most
existing math reasoning datasets may be not able to fully evaluate and analyze
the ability of LLMs in manipulating tools and performing reasoning, as they may
only require very few invocations of tools or miss annotations for evaluating
intermediate reasoning steps. To address the issue, we construct \textbf{CARP},
a new Chinese dataset consisting of 4,886 computation-intensive algebra
problems with formulated annotations on intermediate steps. In CARP, we test
four LLMs with CoT prompting, and find that they are all prone to make mistakes
at the early steps of the solution, leading to wrong answers. Based on this
finding, we propose a new approach that can deliberate the reasoning steps with
tool interfaces, namely \textbf{DELI}. In DELI, we first initialize a
step-by-step solution based on retrieved exemplars, then iterate two
deliberation procedures that check and refine the intermediate steps of the
generated solution, from the perspectives of tool manipulation and natural
language reasoning, until obtaining converged solutions or reaching the maximum
turn. Experimental results on CARP and six other datasets show that the
proposed DELI mostly outperforms competitive baselines, and can further boost
the performance of existing CoT methods. Our data and code are available in
\url{https://github.com/RUCAIBox/CARP}.
|
[
"cs.CL"
] | false |
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