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2306.05307
|
2023-06-08T15:56:57Z
|
Are fairness metric scores enough to assess discrimination biases in
machine learning?
|
[
"Fanny Jourdan",
"Laurent Risser",
"Jean-Michel Loubes",
"Nicholas Asher"
] |
This paper presents novel experiments shedding light on the shortcomings of
current metrics for assessing biases of gender discrimination made by machine
learning algorithms on textual data. We focus on the Bios dataset, and our
learning task is to predict the occupation of individuals, based on their
biography. Such prediction tasks are common in commercial Natural Language
Processing (NLP) applications such as automatic job recommendations. We address
an important limitation of theoretical discussions dealing with group-wise
fairness metrics: they focus on large datasets, although the norm in many
industrial NLP applications is to use small to reasonably large linguistic
datasets for which the main practical constraint is to get a good prediction
accuracy. We then question how reliable are different popular measures of bias
when the size of the training set is simply sufficient to learn reasonably
accurate predictions. Our experiments sample the Bios dataset and learn more
than 200 models on different sample sizes. This allows us to statistically
study our results and to confirm that common gender bias indices provide
diverging and sometimes unreliable results when applied to relatively small
training and test samples. This highlights the crucial importance of variance
calculations for providing sound results in this field.
|
[
"cs.CL",
"cs.CY",
"cs.LG",
"stat.ML"
] | false |
2306.05360
|
2023-06-08T17:05:38Z
|
The ADAIO System at the BEA-2023 Shared Task on Generating AI Teacher
Responses in Educational Dialogues
|
[
"Adaeze Adigwe",
"Zheng Yuan"
] |
This paper presents the ADAIO team's system entry in the Building Educational
Applications (BEA) 2023 Shared Task on Generating AI Teacher Responses in
Educational Dialogues. The task aims to assess the performance of
state-of-the-art generative models as AI teachers in producing suitable
responses within a student-teacher dialogue. Our system comprises evaluating
various baseline models using OpenAI GPT-3 and designing diverse prompts to
prompt the OpenAI models for teacher response generation. After the challenge,
our system achieved second place by employing a few-shot prompt-based approach
with the OpenAI text-davinci-003 model. The results highlight the few-shot
learning capabilities of large-language models, particularly OpenAI's GPT-3, in
the role of AI teachers.
|
[
"cs.CL",
"cs.AI",
"cs.CY"
] | false |
2306.05446
|
2023-06-08T17:28:28Z
|
Latent Phrase Matching for Dysarthric Speech
|
[
"Colin Lea",
"Dianna Yee",
"Jaya Narain",
"Zifang Huang",
"Lauren Tooley",
"Jeffrey P. Bigham",
"Leah Findlater"
] |
Many consumer speech recognition systems are not tuned for people with speech
disabilities, resulting in poor recognition and user experience, especially for
severe speech differences. Recent studies have emphasized interest in
personalized speech models from people with atypical speech patterns. We
propose a query-by-example-based personalized phrase recognition system that is
trained using small amounts of speech, is language agnostic, does not assume a
traditional pronunciation lexicon, and generalizes well across speech
difference severities. On an internal dataset collected from 32 people with
dysarthria, this approach works regardless of severity and shows a 60%
improvement in recall relative to a commercial speech recognition system. On
the public EasyCall dataset of dysarthric speech, our approach improves
accuracy by 30.5%. Performance degrades as the number of phrases increases, but
consistently outperforms ASR systems when trained with 50 unique phrases.
|
[
"eess.AS",
"cs.AI",
"cs.CL",
"cs.LG"
] | false |
2306.05550
|
2023-06-08T20:46:09Z
|
Bias Against 93 Stigmatized Groups in Masked Language Models and
Downstream Sentiment Classification Tasks
|
[
"Katelyn X. Mei",
"Sonia Fereidooni",
"Aylin Caliskan"
] |
The rapid deployment of artificial intelligence (AI) models demands a
thorough investigation of biases and risks inherent in these models to
understand their impact on individuals and society. This study extends the
focus of bias evaluation in extant work by examining bias against social
stigmas on a large scale. It focuses on 93 stigmatized groups in the United
States, including a wide range of conditions related to disease, disability,
drug use, mental illness, religion, sexuality, socioeconomic status, and other
relevant factors. We investigate bias against these groups in English
pre-trained Masked Language Models (MLMs) and their downstream sentiment
classification tasks. To evaluate the presence of bias against 93 stigmatized
conditions, we identify 29 non-stigmatized conditions to conduct a comparative
analysis. Building upon a psychology scale of social rejection, the Social
Distance Scale, we prompt six MLMs: RoBERTa-base, RoBERTa-large, XLNet-large,
BERTweet-base, BERTweet-large, and DistilBERT. We use human annotations to
analyze the predicted words from these models, with which we measure the extent
of bias against stigmatized groups. When prompts include stigmatized
conditions, the probability of MLMs predicting negative words is approximately
20 percent higher than when prompts have non-stigmatized conditions. In the
sentiment classification tasks, when sentences include stigmatized conditions
related to diseases, disability, education, and mental illness, they are more
likely to be classified as negative. We also observe a strong correlation
between bias in MLMs and their downstream sentiment classifiers (r =0.79). The
evidence indicates that MLMs and their downstream sentiment classification
tasks exhibit biases against socially stigmatized groups.
|
[
"cs.CY",
"cs.AI",
"cs.CL",
"cs.LG",
"K.4; I.2.7; I.2.0"
] | false |
2306.07944
|
2023-06-08T22:33:22Z
|
Speech-to-Text Adapter and Speech-to-Entity Retriever Augmented LLMs for
Speech Understanding
|
[
"Mingqiu Wang",
"Izhak Shafran",
"Hagen Soltau",
"Wei Han",
"Yuan Cao",
"Dian Yu",
"Laurent El Shafey"
] |
Large Language Models (LLMs) have been applied in the speech domain, often
incurring a performance drop due to misaligned between speech and language
representations. To bridge this gap, we propose a joint speech and language
model (SLM) using a Speech2Text adapter, which maps speech into text token
embedding space without speech information loss. Additionally, using a
CTC-based blank-filtering, we can reduce the speech sequence length to that of
text. In speech MultiWoz dataset (DSTC11 challenge), SLM largely improves the
dialog state tracking (DST) performance (24.7% to 28.4% accuracy). Further to
address errors on rare entities, we augment SLM with a Speech2Entity retriever,
which uses speech to retrieve relevant entities, and then adds them to the
original SLM input as a prefix. With this retrieval-augmented SLM (ReSLM), the
DST performance jumps to 34.6% accuracy. Moreover, augmenting the ASR task with
the dialog understanding task improves the ASR performance from 9.4% to 8.5%
WER.
|
[
"eess.AS",
"cs.AI",
"cs.CL"
] | true |
2306.04875
|
2023-06-08T02:12:26Z
|
Instructed Diffuser with Temporal Condition Guidance for Offline
Reinforcement Learning
|
[
"Jifeng Hu",
"Yanchao Sun",
"Sili Huang",
"SiYuan Guo",
"Hechang Chen",
"Li Shen",
"Lichao Sun",
"Yi Chang",
"Dacheng Tao"
] |
Recent works have shown the potential of diffusion models in computer vision
and natural language processing. Apart from the classical supervised learning
fields, diffusion models have also shown strong competitiveness in
reinforcement learning (RL) by formulating decision-making as sequential
generation. However, incorporating temporal information of sequential data and
utilizing it to guide diffusion models to perform better generation is still an
open challenge. In this paper, we take one step forward to investigate
controllable generation with temporal conditions that are refined from temporal
information. We observe the importance of temporal conditions in sequential
generation in sufficient explorative scenarios and provide a comprehensive
discussion and comparison of different temporal conditions. Based on the
observations, we propose an effective temporally-conditional diffusion model
coined Temporally-Composable Diffuser (TCD), which extracts temporal
information from interaction sequences and explicitly guides generation with
temporal conditions. Specifically, we separate the sequences into three parts
according to time expansion and identify historical, immediate, and prospective
conditions accordingly. Each condition preserves non-overlapping temporal
information of sequences, enabling more controllable generation when we jointly
use them to guide the diffuser. Finally, we conduct extensive experiments and
analysis to reveal the favorable applicability of TCD in offline RL tasks,
where our method reaches or matches the best performance compared with prior
SOTA baselines.
|
[
"cs.LG"
] | false |
2306.04879
|
2023-06-08T02:18:58Z
|
Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision
Post-Training Quantization
|
[
"Clemens JS Schaefer",
"Navid Lambert-Shirzad",
"Xiaofan Zhang",
"Chiachen Chou",
"Tom Jablin",
"Jian Li",
"Elfie Guo",
"Caitlin Stanton",
"Siddharth Joshi",
"Yu Emma Wang"
] |
Efficiently serving neural network models with low latency is becoming more
challenging due to increasing model complexity and parameter count. Model
quantization offers a solution which simultaneously reduces memory footprint
and compute requirements. However, aggressive quantization may lead to an
unacceptable loss in model accuracy owing to differences in sensitivity to
numerical imperfection across different layers in the model. To address this
challenge, we propose a mixed-precision post training quantization (PTQ)
approach that assigns different numerical precisions to tensors in a network
based on their specific needs, for a reduced memory footprint and improved
latency while preserving model accuracy. Previous works rely on layer-wise
Hessian information to determine numerical precision, but as we demonstrate,
Hessian estimation is typically insufficient in determining an effective
ordering of layer sensitivities. We address this by augmenting the estimated
Hessian with additional information to capture inter-layer dependencies. We
demonstrate that this consistently improves PTQ performance along the
accuracy-latency Pareto frontier across multiple models. Our method combines
second-order information and inter-layer dependencies to guide a bisection
search, finding quantization configurations within a user-configurable model
accuracy degradation range. We evaluate the effectiveness of our method on the
ResNet50, MobileNetV2, and BERT models. Our experiments demonstrate latency
reductions compared to a 16-bit baseline of $25.48\%$, $21.69\%$, and $33.28\%$
respectively, while maintaining model accuracy to within $99.99\%$ of the
baseline model.
|
[
"cs.LG"
] | false |
2306.04994
|
2023-06-08T07:29:42Z
|
Ambulance Demand Prediction via Convolutional Neural Networks
|
[
"Maximiliane Rautenstrauß",
"Maximilian Schiffer"
] |
Minimizing response times is crucial for emergency medical services to reduce
patients' waiting times and to increase their survival rates. Many models exist
to optimize operational tasks such as ambulance allocation and dispatching.
Including accurate demand forecasts in such models can improve operational
decision-making. Against this background, we present a novel convolutional
neural network (CNN) architecture that transforms time series data into
heatmaps to predict ambulance demand. Applying such predictions requires
incorporating external features that influence ambulance demands. We contribute
to the existing literature by providing a flexible, generic CNN architecture,
allowing for the inclusion of external features with varying dimensions.
Additionally, we provide a feature selection and hyperparameter optimization
framework utilizing Bayesian optimization. We integrate historical ambulance
demand and external information such as weather, events, holidays, and time. To
show the superiority of the developed CNN architecture over existing
approaches, we conduct a case study for Seattle's 911 call data and include
external information. We show that the developed CNN architecture outperforms
existing state-of-the-art methods and industry practice by more than 9%.
|
[
"cs.LG"
] | false |
2306.05043
|
2023-06-08T08:53:59Z
|
Non-autoregressive Conditional Diffusion Models for Time Series
Prediction
|
[
"Lifeng Shen",
"James Kwok"
] |
Recently, denoising diffusion models have led to significant breakthroughs in
the generation of images, audio and text. However, it is still an open question
on how to adapt their strong modeling ability to model time series. In this
paper, we propose TimeDiff, a non-autoregressive diffusion model that achieves
high-quality time series prediction with the introduction of two novel
conditioning mechanisms: future mixup and autoregressive initialization.
Similar to teacher forcing, future mixup allows parts of the ground-truth
future predictions for conditioning, while autoregressive initialization helps
better initialize the model with basic time series patterns such as short-term
trends. Extensive experiments are performed on nine real-world datasets.
Results show that TimeDiff consistently outperforms existing time series
diffusion models, and also achieves the best overall performance across a
variety of the existing strong baselines (including transformers and FiLM).
|
[
"cs.LG"
] | false |
2306.05046
|
2023-06-08T08:57:06Z
|
A Gradient-based Approach for Online Robust Deep Neural Network Training
with Noisy Labels
|
[
"Yifan Yang",
"Alec Koppel",
"Zheng Zhang"
] |
Learning with noisy labels is an important topic for scalable training in
many real-world scenarios. However, few previous research considers this
problem in the online setting, where the arrival of data is streaming. In this
paper, we propose a novel gradient-based approach to enable the detection of
noisy labels for the online learning of model parameters, named Online
Gradient-based Robust Selection (OGRS). In contrast to the previous sample
selection approach for the offline training that requires the estimation of a
clean ratio of the dataset before each epoch of training, OGRS can
automatically select clean samples by steps of gradient update from datasets
with varying clean ratios without changing the parameter setting. During the
training process, the OGRS method selects clean samples at each iteration and
feeds the selected sample to incrementally update the model parameters. We
provide a detailed theoretical analysis to demonstrate data selection process
is converging to the low-loss region of the sample space, by introducing and
proving the sub-linear local Lagrangian regret of the non-convex constrained
optimization problem. Experimental results show that it outperforms
state-of-the-art methods in different settings.
|
[
"cs.LG"
] | false |
2306.05060
|
2023-06-08T09:23:46Z
|
Precision-aware Latency and Energy Balancing on Multi-Accelerator
Platforms for DNN Inference
|
[
"Matteo Risso",
"Alessio Burrello",
"Giuseppe Maria Sarda",
"Luca Benini",
"Enrico Macii",
"Massimo Poncino",
"Marian Verhelst",
"Daniele Jahier Pagliari"
] |
The need to execute Deep Neural Networks (DNNs) at low latency and low power
at the edge has spurred the development of new heterogeneous Systems-on-Chips
(SoCs) encapsulating a diverse set of hardware accelerators. How to optimally
map a DNN onto such multi-accelerator systems is an open problem. We propose
ODiMO, a hardware-aware tool that performs a fine-grain mapping across
different accelerators on-chip, splitting individual layers and executing them
in parallel, to reduce inference energy consumption or latency, while taking
into account each accelerator's quantization precision to maintain accuracy.
Pareto-optimal networks in the accuracy vs. energy or latency space are pursued
for three popular dataset/DNN pairs, and deployed on the DIANA heterogeneous
ultra-low power edge AI SoC. We show that ODiMO reduces energy/latency by up to
33%/31% with limited accuracy drop (-0.53%/-0.32%) compared to manual heuristic
mappings.
|
[
"cs.LG"
] | false |
2306.05101
|
2023-06-08T10:59:35Z
|
Sy-CON: Symmetric Contrastive Loss for Continual Self-Supervised
Representation Learning
|
[
"Sungmin Cha",
"Taesup Moon"
] |
We introduce a novel and general loss function, called Symmetric Contrastive
(Sy-CON) loss, for effective continual self-supervised learning (CSSL). We
first argue that the conventional loss form of continual learning which
consists of single task-specific loss (for plasticity) and a regularizer (for
stability) may not be ideal for contrastive loss based CSSL that focus on
representation learning. Our reasoning is that, in contrastive learning based
methods, the task-specific loss would suffer from decreasing diversity of
negative samples and the regularizer may hinder learning new distinctive
representations. To that end, we propose Sy-CON that consists of two losses
(one for plasticity and the other for stability) with symmetric dependence on
current and past models' negative sample embeddings. We argue our model can
naturally find good trade-off between the plasticity and stability without any
explicit hyperparameter tuning. We validate the effectiveness of our approach
through extensive experiments, demonstrating that MoCo-based implementation of
Sy-CON loss achieves superior performance compared to other state-of-the-art
CSSL methods.
|
[
"cs.LG"
] | false |
2306.05123
|
2023-06-08T11:47:02Z
|
A Meta-Generation framework for Industrial System Generation
|
[
"Fouad Oubari",
"Raphael Meunier",
"Rodrigue Décatoire",
"Mathilde Mougeot"
] |
Generative design is an increasingly important tool in the industrial world.
It allows the designers and engineers to easily explore vast ranges of design
options, providing a cheaper and faster alternative to the trial and failure
approaches. Thanks to the flexibility they offer, Deep Generative Models are
gaining popularity amongst Generative Design technologies. However, developing
and evaluating these models can be challenging. The field lacks accessible
benchmarks, in order to evaluate and compare objectively different Deep
Generative Models architectures. Moreover, vanilla Deep Generative Models
appear to be unable to accurately generate multi-components industrial systems
that are controlled by latent design constraints. To address these challenges,
we propose an industry-inspired use case that incorporates actual industrial
system characteristics. This use case can be quickly generated and used as a
benchmark. We propose a Meta-VAE capable of producing multi-component
industrial systems and showcase its application on the proposed use case.
|
[
"cs.LG"
] | false |
2306.05167
|
2023-06-08T13:03:53Z
|
Decision S4: Efficient Sequence-Based RL via State Spaces Layers
|
[
"Shmuel Bar-David",
"Itamar Zimerman",
"Eliya Nachmani",
"Lior Wolf"
] |
Recently, sequence learning methods have been applied to the problem of
off-policy Reinforcement Learning, including the seminal work on Decision
Transformers, which employs transformers for this task. Since transformers are
parameter-heavy, cannot benefit from history longer than a fixed window size,
and are not computed using recurrence, we set out to investigate the
suitability of the S4 family of models, which are based on state-space layers
and have been shown to outperform transformers, especially in modeling
long-range dependencies. In this work we present two main algorithms: (i) an
off-policy training procedure that works with trajectories, while still
maintaining the training efficiency of the S4 model. (ii) An on-policy training
procedure that is trained in a recurrent manner, benefits from long-range
dependencies, and is based on a novel stable actor-critic mechanism. Our
results indicate that our method outperforms multiple variants of decision
transformers, as well as the other baseline methods on most tasks, while
reducing the latency, number of parameters, and training time by several orders
of magnitude, making our approach more suitable for real-world RL.
|
[
"cs.LG",
"14J60",
"F.2.2; I.2.7"
] | false |
2306.05211
|
2023-06-08T14:09:38Z
|
Boosting-based Construction of BDDs for Linear Threshold Functions and
Its Application to Verification of Neural Networks
|
[
"Yiping Tang",
"Kohei Hatano",
"Eiji Takimoto"
] |
Understanding the characteristics of neural networks is important but
difficult due to their complex structures and behaviors. Some previous work
proposes to transform neural networks into equivalent Boolean expressions and
apply verification techniques for characteristics of interest. This approach is
promising since rich results of verification techniques for circuits and other
Boolean expressions can be readily applied. The bottleneck is the time
complexity of the transformation. More precisely, (i) each neuron of the
network, i.e., a linear threshold function, is converted to a Binary Decision
Diagram (BDD), and (ii) they are further combined into some final form, such as
Boolean circuits. For a linear threshold function with $n$ variables, an
existing method takes $O(n2^{\frac{n}{2}})$ time to construct an ordered BDD of
size $O(2^{\frac{n}{2}})$ consistent with some variable ordering. However, it
is non-trivial to choose a variable ordering producing a small BDD among $n!$
candidates.
We propose a method to convert a linear threshold function to a specific form
of a BDD based on the boosting approach in the machine learning literature. Our
method takes $O(2^n \text{poly}(1/\rho))$ time and outputs BDD of size
$O(\frac{n^2}{\rho^4}\ln{\frac{1}{\rho}})$, where $\rho$ is the margin of some
consistent linear threshold function. Our method does not need to search for
good variable orderings and produces a smaller expression when the margin of
the linear threshold function is large. More precisely, our method is based on
our new boosting algorithm, which is of independent interest. We also propose a
method to combine them into the final Boolean expression representing the
neural network.
|
[
"cs.LG"
] | false |
2306.05310
|
2023-06-08T15:59:31Z
|
A framework for dynamically training and adapting deep reinforcement
learning models to different, low-compute, and continuously changing
radiology deployment environments
|
[
"Guangyao Zheng",
"Shuhao Lai",
"Vladimir Braverman",
"Michael A. Jacobs",
"Vishwa S. Parekh"
] |
While Deep Reinforcement Learning has been widely researched in medical
imaging, the training and deployment of these models usually require powerful
GPUs. Since imaging environments evolve rapidly and can be generated by edge
devices, the algorithm is required to continually learn and adapt to changing
environments, and adjust to low-compute devices. To this end, we developed
three image coreset algorithms to compress and denoise medical images for
selective experience replayed-based lifelong reinforcement learning. We
implemented neighborhood averaging coreset, neighborhood sensitivity-based
sampling coreset, and maximum entropy coreset on full-body DIXON water and
DIXON fat MRI images. All three coresets produced 27x compression with
excellent performance in localizing five anatomical landmarks: left knee, right
trochanter, left kidney, spleen, and lung across both imaging environments.
Maximum entropy coreset obtained the best performance of $11.97\pm 12.02$
average distance error, compared to the conventional lifelong learning
framework's $19.24\pm 50.77$.
|
[
"cs.LG"
] | false |
2306.05325
|
2023-06-08T16:18:08Z
|
Federated Learning under Covariate Shifts with Generalization Guarantees
|
[
"Ali Ramezani-Kebrya",
"Fanghui Liu",
"Thomas Pethick",
"Grigorios Chrysos",
"Volkan Cevher"
] |
This paper addresses intra-client and inter-client covariate shifts in
federated learning (FL) with a focus on the overall generalization performance.
To handle covariate shifts, we formulate a new global model training paradigm
and propose Federated Importance-Weighted Empirical Risk Minimization (FTW-ERM)
along with improving density ratio matching methods without requiring perfect
knowledge of the supremum over true ratios. We also propose the
communication-efficient variant FITW-ERM with the same level of privacy
guarantees as those of classical ERM in FL. We theoretically show that FTW-ERM
achieves smaller generalization error than classical ERM under certain
settings. Experimental results demonstrate the superiority of FTW-ERM over
existing FL baselines in challenging imbalanced federated settings in terms of
data distribution shifts across clients.
|
[
"cs.LG"
] | false |
2306.05483
|
2023-06-08T18:07:02Z
|
On the Importance of Exploration for Generalization in Reinforcement
Learning
|
[
"Yiding Jiang",
"J. Zico Kolter",
"Roberta Raileanu"
] |
Existing approaches for improving generalization in deep reinforcement
learning (RL) have mostly focused on representation learning, neglecting
RL-specific aspects such as exploration. We hypothesize that the agent's
exploration strategy plays a key role in its ability to generalize to new
environments. Through a series of experiments in a tabular contextual MDP, we
show that exploration is helpful not only for efficiently finding the optimal
policy for the training environments but also for acquiring knowledge that
helps decision making in unseen environments. Based on these observations, we
propose EDE: Exploration via Distributional Ensemble, a method that encourages
exploration of states with high epistemic uncertainty through an ensemble of
Q-value distributions. Our algorithm is the first value-based approach to
achieve state-of-the-art on both Procgen and Crafter, two benchmarks for
generalization in RL with high-dimensional observations. The open-sourced
implementation can be found at https://github.com/facebookresearch/ede .
|
[
"cs.LG"
] | false |
2306.04862
|
2023-06-08T01:26:22Z
|
A Systematic Literature Review on Client Selection in Federated Learning
|
[
"Carl Smestad",
"Jingyue Li"
] |
With the arising concerns of privacy within machine learning, federated
learning (FL) was invented in 2017, in which the clients, such as mobile
devices, train a model and send the update to the centralized server. Choosing
clients randomly for FL can harm learning performance due to different reasons.
Many studies have proposed approaches to address the challenges of client
selection of FL. However, no systematic literature review (SLR) on this topic
existed. This SLR investigates the state of the art of client selection in FL
and answers the challenges, solutions, and metrics to evaluate the solutions.
We systematically reviewed 47 primary studies. The main challenges found in
client selection are heterogeneity, resource allocation, communication costs,
and fairness. The client selection schemes aim to improve the original random
selection algorithm by focusing on one or several of the aforementioned
challenges. The most common metric used is testing accuracy versus
communication rounds, as testing accuracy measures the successfulness of the
learning and preferably in as few communication rounds as possible, as they are
very expensive. Although several possible improvements can be made with the
current state of client selection, the most beneficial ones are evaluating the
impact of unsuccessful clients and gaining a more theoretical understanding of
the impact of fairness in FL.
|
[
"cs.LG",
"cs.AI"
] | false |
2306.04894
|
2023-06-08T02:48:37Z
|
A Bayesian Framework for learning governing Partial Differential
Equation from Data
|
[
"Kalpesh More",
"Tapas Tripura",
"Rajdip Nayek",
"Souvik Chakraborty"
] |
The discovery of partial differential equations (PDEs) is a challenging task
that involves both theoretical and empirical methods. Machine learning
approaches have been developed and used to solve this problem; however, it is
important to note that existing methods often struggle to identify the
underlying equation accurately in the presence of noise. In this study, we
present a new approach to discovering PDEs by combining variational Bayes and
sparse linear regression. The problem of PDE discovery has been posed as a
problem to learn relevant basis from a predefined dictionary of basis
functions. To accelerate the overall process, a variational Bayes-based
approach for discovering partial differential equations is proposed. To ensure
sparsity, we employ a spike and slab prior. We illustrate the efficacy of our
strategy in several examples, including Burgers, Korteweg-de Vries, Kuramoto
Sivashinsky, wave equation, and heat equation (1D as well as 2D). Our method
offers a promising avenue for discovering PDEs from data and has potential
applications in fields such as physics, engineering, and biology.
|
[
"stat.ML",
"cs.LG"
] | false |
2306.04895
|
2023-06-08T02:49:12Z
|
Solution of physics-based inverse problems using conditional generative
adversarial networks with full gradient penalty
|
[
"Deep Ray",
"Javier Murgoitio-Esandi",
"Agnimitra Dasgupta",
"Assad A. Oberai"
] |
The solution of probabilistic inverse problems for which the corresponding
forward problem is constrained by physical principles is challenging. This is
especially true if the dimension of the inferred vector is large and the prior
information about it is in the form of a collection of samples. In this work, a
novel deep learning based approach is developed and applied to solving these
types of problems. The approach utilizes samples of the inferred vector drawn
from the prior distribution and a physics-based forward model to generate
training data for a conditional Wasserstein generative adversarial network
(cWGAN). The cWGAN learns the probability distribution for the inferred vector
conditioned on the measurement and produces samples from this distribution. The
cWGAN developed in this work differs from earlier versions in that its critic
is required to be 1-Lipschitz with respect to both the inferred and the
measurement vectors and not just the former. This leads to a loss term with the
full (and not partial) gradient penalty. It is shown that this rather simple
change leads to a stronger notion of convergence for the conditional density
learned by the cWGAN and a more robust and accurate sampling strategy. Through
numerical examples it is shown that this change also translates to better
accuracy when solving inverse problems. The numerical examples considered
include illustrative problems where the true distribution and/or statistics are
known, and a more complex inverse problem motivated by applications in
biomechanics.
|
[
"stat.ML",
"cs.LG"
] | false |
2306.04948
|
2023-06-08T05:41:42Z
|
ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton
Tactical Analysis
|
[
"Wei-Yao Wang",
"Yung-Chang Huang",
"Tsi-Ui Ik",
"Wen-Chih Peng"
] |
With the recent progress in sports analytics, deep learning approaches have
demonstrated the effectiveness of mining insights into players' tactics for
improving performance quality and fan engagement. This is attributed to the
availability of public ground-truth datasets. While there are a few available
datasets for turn-based sports for action detection, these datasets severely
lack structured source data and stroke-level records since these require
high-cost labeling efforts from domain experts and are hard to detect using
automatic techniques. Consequently, the development of artificial intelligence
approaches is significantly hindered when existing models are applied to more
challenging structured turn-based sequences. In this paper, we present
ShuttleSet, the largest publicly-available badminton singles dataset with
annotated stroke-level records. It contains 104 sets, 3,685 rallies, and 36,492
strokes in 44 matches between 2018 and 2021 with 27 top-ranking men's singles
and women's singles players. ShuttleSet is manually annotated with a
computer-aided labeling tool to increase the labeling efficiency and
effectiveness of selecting the shot type with a choice of 18 distinct classes,
the corresponding hitting locations, and the locations of both players at each
stroke. In the experiments, we provide multiple benchmarks (i.e., stroke
influence, stroke forecasting, and movement forecasting) with baselines to
illustrate the practicability of using ShuttleSet for turn-based analytics,
which is expected to stimulate both academic and sports communities. Over the
past two years, a visualization platform has been deployed to illustrate the
variability of analysis cases from ShuttleSet for coaches to delve into
players' tactical preferences with human-interactive interfaces, which was also
used by national badminton teams during multiple international high-ranking
matches.
|
[
"cs.LG",
"cs.AI"
] | false |
2306.04952
|
2023-06-08T05:56:05Z
|
Entropy-based Training Methods for Scalable Neural Implicit Sampler
|
[
"Weijian Luo",
"Boya Zhang",
"Zhihua Zhang"
] |
Efficiently sampling from un-normalized target distributions is a fundamental
problem in scientific computing and machine learning. Traditional approaches
like Markov Chain Monte Carlo (MCMC) guarantee asymptotically unbiased samples
from such distributions but suffer from computational inefficiency,
particularly when dealing with high-dimensional targets, as they require
numerous iterations to generate a batch of samples. In this paper, we propose
an efficient and scalable neural implicit sampler that overcomes these
limitations. Our sampler can generate large batches of samples with low
computational costs by leveraging a neural transformation that directly maps
easily sampled latent vectors to target samples without the need for iterative
procedures. To train the neural implicit sampler, we introduce two novel
methods: the KL training method and the Fisher training method. The former
minimizes the Kullback-Leibler divergence, while the latter minimizes the
Fisher divergence. By employing these training methods, we effectively optimize
the neural implicit sampler to capture the desired target distribution. To
demonstrate the effectiveness, efficiency, and scalability of our proposed
samplers, we evaluate them on three sampling benchmarks with different scales.
These benchmarks include sampling from 2D targets, Bayesian inference, and
sampling from high-dimensional energy-based models (EBMs). Notably, in the
experiment involving high-dimensional EBMs, our sampler produces samples that
are comparable to those generated by MCMC-based methods while being more than
100 times more efficient, showcasing the efficiency of our neural sampler. We
believe that the theoretical and empirical contributions presented in this work
will stimulate further research on developing efficient samplers for various
applications beyond the ones explored in this study.
|
[
"stat.ML",
"cs.LG"
] | false |
2306.04962
|
2023-06-08T06:37:04Z
|
arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering
|
[
"Meng Liu",
"Ke Liang",
"Yue Liu",
"Siwei Wang",
"Sihang Zhou",
"Xinwang Liu"
] |
Temporal graph clustering (TGC) is a crucial task in temporal graph learning.
Its focus is on node clustering on temporal graphs, and it offers greater
flexibility for large-scale graph structures due to the mechanism of temporal
graph methods. However, the development of TGC is currently constrained by a
significant problem: the lack of suitable and reliable large-scale temporal
graph datasets to evaluate clustering performance. In other words, most
existing temporal graph datasets are in small sizes, and even large-scale
datasets contain only a limited number of available node labels. It makes
evaluating models for large-scale temporal graph clustering challenging. To
address this challenge, we build arXiv4TGC, a set of novel academic datasets
(including arXivAI, arXivCS, arXivMath, arXivPhy, and arXivLarge) for
large-scale temporal graph clustering. In particular, the largest dataset,
arXivLarge, contains 1.3 million labeled available nodes and 10 million
temporal edges. We further compare the clustering performance with typical
temporal graph learning models on both previous classic temporal graph datasets
and the new datasets proposed in this paper. The clustering performance on
arXiv4TGC can be more apparent for evaluating different models, resulting in
higher clustering confidence and more suitable for large-scale temporal graph
clustering. The arXiv4TGC datasets are publicly available at:
https://github.com/MGitHubL/arXiv4TGC.
|
[
"cs.AI",
"cs.LG"
] | false |
2306.04971
|
2023-06-08T06:59:21Z
|
A Melting Pot of Evolution and Learning
|
[
"Moshe Sipper",
"Achiya Elyasaf",
"Tomer Halperin",
"Zvika Haramaty",
"Raz Lapid",
"Eyal Segal",
"Itai Tzruia",
"Snir Vitrack Tamam"
] |
We survey eight recent works by our group, involving the successful blending
of evolutionary algorithms with machine learning and deep learning: 1. Binary
and Multinomial Classification through Evolutionary Symbolic Regression, 2.
Classy Ensemble: A Novel Ensemble Algorithm for Classification, 3. EC-KitY:
Evolutionary Computation Tool Kit in Python, 4. Evolution of Activation
Functions for Deep Learning-Based Image Classification, 5. Adaptive Combination
of a Genetic Algorithm and Novelty Search for Deep Neuroevolution, 6. An
Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for
Generating Adversarial Instances in Deep Networks, 7. Foiling Explanations in
Deep Neural Networks, 8. Patch of Invisibility: Naturalistic Black-Box
Adversarial Attacks on Object Detectors.
|
[
"cs.NE",
"cs.LG"
] | false |
2306.04974
|
2023-06-08T07:05:36Z
|
Conservative Prediction via Data-Driven Confidence Minimization
|
[
"Caroline Choi",
"Fahim Tajwar",
"Yoonho Lee",
"Huaxiu Yao",
"Ananya Kumar",
"Chelsea Finn"
] |
Errors of machine learning models are costly, especially in safety-critical
domains such as healthcare, where such mistakes can prevent the deployment of
machine learning altogether. In these settings, conservative models -- models
which can defer to human judgment when they are likely to make an error -- may
offer a solution. However, detecting unusual or difficult examples is notably
challenging, as it is impossible to anticipate all potential inputs at test
time. To address this issue, prior work has proposed to minimize the model's
confidence on an auxiliary pseudo-OOD dataset. We theoretically analyze the
effect of confidence minimization and show that the choice of auxiliary dataset
is critical. Specifically, if the auxiliary dataset includes samples from the
OOD region of interest, confidence minimization provably separates ID and OOD
inputs by predictive confidence. Taking inspiration from this result, we
present data-driven confidence minimization (DCM), which minimizes confidence
on an uncertainty dataset containing examples that the model is likely to
misclassify at test time. Our experiments show that DCM consistently
outperforms state-of-the-art OOD detection methods on 8 ID-OOD dataset pairs,
reducing FPR (at TPR 95%) by 6.3% and 58.1% on CIFAR-10 and CIFAR-100, and
outperforms existing selective classification approaches on 4 datasets in
conditions of distribution shift.
|
[
"cs.LG",
"cs.AI"
] | false |
2306.05011
|
2023-06-08T07:59:08Z
|
Attention Weighted Mixture of Experts with Contrastive Learning for
Personalized Ranking in E-commerce
|
[
"Juan Gong",
"Zhenlin Chen",
"Chaoyi Ma",
"Zhuojian Xiao",
"Haonan Wang",
"Guoyu Tang",
"Lin Liu",
"Sulong Xu",
"Bo Long",
"Yunjiang Jiang"
] |
Ranking model plays an essential role in e-commerce search and
recommendation. An effective ranking model should give a personalized ranking
list for each user according to the user preference. Existing algorithms
usually extract a user representation vector from the user behavior sequence,
then feed the vector into a feed-forward network (FFN) together with other
features for feature interactions, and finally produce a personalized ranking
score. Despite tremendous progress in the past, there is still room for
improvement. Firstly, the personalized patterns of feature interactions for
different users are not explicitly modeled. Secondly, most of existing
algorithms have poor personalized ranking results for long-tail users with few
historical behaviors due to the data sparsity. To overcome the two challenges,
we propose Attention Weighted Mixture of Experts (AW-MoE) with contrastive
learning for personalized ranking. Firstly, AW-MoE leverages the MoE framework
to capture personalized feature interactions for different users. To model the
user preference, the user behavior sequence is simultaneously fed into expert
networks and the gate network. Within the gate network, one gate unit and one
activation unit are designed to adaptively learn the fine-grained activation
vector for experts using an attention mechanism. Secondly, a random masking
strategy is applied to the user behavior sequence to simulate long-tail users,
and an auxiliary contrastive loss is imposed to the output of the gate network
to improve the model generalization for these users. This is validated by a
higher performance gain on the long-tail user test set. Experiment results on a
JD real production dataset and a public dataset demonstrate the effectiveness
of AW-MoE, which significantly outperforms state-of-art methods. Notably,
AW-MoE has been successfully deployed in the JD e-commerce search engine, ...
|
[
"cs.IR",
"cs.LG"
] | false |
2306.05017
|
2023-06-08T08:11:21Z
|
Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A
Review
|
[
"Mohammad Irani Azad",
"Roozbeh Rajabi",
"Abouzar Estebsari"
] |
Demand-side management now encompasses more residential loads. To efficiently
apply demand response strategies, it's essential to periodically observe the
contribution of various domestic appliances to total energy consumption.
Non-intrusive load monitoring (NILM), also known as load disaggregation, is a
method for decomposing the total energy consumption profile into individual
appliance load profiles within the household. It has multiple applications in
demand-side management, energy consumption monitoring, and analysis. Various
methods, including machine learning and deep learning, have been used to
implement and improve NILM algorithms. This paper reviews some recent NILM
methods based on deep learning and introduces the most accurate methods for
residential loads. It summarizes public databases for NILM evaluation and
compares methods using standard performance metrics.
|
[
"eess.SP",
"cs.LG"
] | false |
2306.05082
|
2023-06-08T10:20:08Z
|
The Importance of Time in Causal Algorithmic Recourse
|
[
"Isacco Beretta",
"Martina Cinquini"
] |
The application of Algorithmic Recourse in decision-making is a promising
field that offers practical solutions to reverse unfavorable decisions.
However, the inability of these methods to consider potential dependencies
among variables poses a significant challenge due to the assumption of feature
independence. Recent advancements have incorporated knowledge of causal
dependencies, thereby enhancing the quality of the recommended recourse
actions. Despite these improvements, the inability to incorporate the temporal
dimension remains a significant limitation of these approaches. This is
particularly problematic as identifying and addressing the root causes of
undesired outcomes requires understanding time-dependent relationships between
variables. In this work, we motivate the need to integrate the temporal
dimension into causal algorithmic recourse methods to enhance recommendations'
plausibility and reliability. The experimental evaluation highlights the
significance of the role of time in this field.
|
[
"cs.AI",
"cs.LG"
] | false |
2306.05257
|
2023-06-08T14:54:50Z
|
Comprehensive evaluation of deep and graph learning on drug-drug
interactions prediction
|
[
"Xuan Lin",
"Lichang Dai",
"Yafang Zhou",
"Zu-Guo Yu",
"Wen Zhang",
"Jian-Yu Shi",
"Dong-Sheng Cao",
"Li Zeng",
"Haowen Chen",
"Bosheng Song",
"Philip S. Yu",
"Xiangxiang Zeng"
] |
Recent advances and achievements of artificial intelligence (AI) as well as
deep and graph learning models have established their usefulness in biomedical
applications, especially in drug-drug interactions (DDIs). DDIs refer to a
change in the effect of one drug to the presence of another drug in the human
body, which plays an essential role in drug discovery and clinical research.
DDIs prediction through traditional clinical trials and experiments is an
expensive and time-consuming process. To correctly apply the advanced AI and
deep learning, the developer and user meet various challenges such as the
availability and encoding of data resources, and the design of computational
methods. This review summarizes chemical structure based, network based, NLP
based and hybrid methods, providing an updated and accessible guide to the
broad researchers and development community with different domain knowledge. We
introduce widely-used molecular representation and describe the theoretical
frameworks of graph neural network models for representing molecular
structures. We present the advantages and disadvantages of deep and graph
learning methods by performing comparative experiments. We discuss the
potential technical challenges and highlight future directions of deep and
graph learning models for accelerating DDIs prediction.
|
[
"cs.LG",
"q-bio.QM"
] | false |
2306.05300
|
2023-06-08T15:45:57Z
|
Correlated Noise in Epoch-Based Stochastic Gradient Descent:
Implications for Weight Variances
|
[
"Marcel Kühn",
"Bernd Rosenow"
] |
Stochastic gradient descent (SGD) has become a cornerstone of neural network
optimization, yet the noise introduced by SGD is often assumed to be
uncorrelated over time, despite the ubiquity of epoch-based training. In this
work, we challenge this assumption and investigate the effects of epoch-based
noise correlations on the stationary distribution of discrete-time SGD with
momentum, limited to a quadratic loss. Our main contributions are twofold:
first, we calculate the exact autocorrelation of the noise for training in
epochs under the assumption that the noise is independent of small fluctuations
in the weight vector; second, we explore the influence of correlations
introduced by the epoch-based learning scheme on SGD dynamics. We find that for
directions with a curvature greater than a hyperparameter-dependent crossover
value, the results for uncorrelated noise are recovered. However, for
relatively flat directions, the weight variance is significantly reduced. We
provide an intuitive explanation for these results based on a crossover between
correlation times, contributing to a deeper understanding of the dynamics of
SGD in the presence of epoch-based noise correlations.
|
[
"cs.LG",
"cond-mat.dis-nn"
] | false |
2306.05319
|
2023-06-08T16:11:57Z
|
RNN-Based GNSS Positioning using Satellite Measurement Features and
Pseudorange Residuals
|
[
"Ibrahim Sbeity",
"Christophe Villien",
"Benoît Denis",
"E. Veronica Belmega"
] |
In the Global Navigation Satellite System (GNSS) context, the growing number
of available satellites has lead to many challenges when it comes to choosing
the most accurate pseudorange contributions, given the strong impact of biased
measurements on positioning accuracy, particularly in single-epoch scenarios.
This work leverages the potential of machine learning in predicting link-wise
measurement quality factors and, hence, optimize measurement weighting. For
this purpose, we use a customized matrix composed of heterogeneous features
such as conditional pseudorange residuals and per-link satellite metrics (e.g.,
carrier-to-noise power density ratio and its empirical statistics, satellite
elevation, carrier phase lock time). This matrix is then fed as an input to a
recurrent neural network (RNN) (i.e., a long-short term memory (LSTM) network).
Our experimental results on real data, obtained from extensive field
measurements, demonstrate the high potential of our proposed solution being
able to outperform traditional measurements weighting and selection strategies
from state-of-the-art.
|
[
"eess.SP",
"cs.LG"
] | false |
2306.05438
|
2023-06-08T06:57:07Z
|
DynamoRep: Trajectory-Based Population Dynamics for Classification of
Black-box Optimization Problems
|
[
"Gjorgjina Cenikj",
"Gašper Petelin",
"Carola Doerr",
"Peter Korošec",
"Tome Eftimov"
] |
The application of machine learning (ML) models to the analysis of
optimization algorithms requires the representation of optimization problems
using numerical features. These features can be used as input for ML models
that are trained to select or to configure a suitable algorithm for the problem
at hand. Since in pure black-box optimization information about the problem
instance can only be obtained through function evaluation, a common approach is
to dedicate some function evaluations for feature extraction, e.g., using
random sampling. This approach has two key downsides: (1) It reduces the budget
left for the actual optimization phase, and (2) it neglects valuable
information that could be obtained from a problem-solver interaction.
In this paper, we propose a feature extraction method that describes the
trajectories of optimization algorithms using simple descriptive statistics. We
evaluate the generated features for the task of classifying problem classes
from the Black Box Optimization Benchmarking (BBOB) suite. We demonstrate that
the proposed DynamoRep features capture enough information to identify the
problem class on which the optimization algorithm is running, achieving a mean
classification accuracy of 95% across all experiments.
|
[
"cs.LG",
"cs.NE"
] | false |
2306.05478
|
2023-06-08T18:03:48Z
|
Trajectory Prediction with Observations of Variable-Length for Motion
Planning in Highway Merging scenarios
|
[
"Sajjad Mozaffari",
"Mreza Alipour Sormoli",
"Konstantinos Koufos",
"Graham Lee",
"Mehrdad Dianati"
] |
Accurate trajectory prediction of nearby vehicles is crucial for the safe
motion planning of automated vehicles in dynamic driving scenarios such as
highway merging. Existing methods cannot initiate prediction for a vehicle
unless observed for a fixed duration of two or more seconds. This prevents a
fast reaction by the ego vehicle to vehicles that enter its perception range,
thus creating safety concerns. Therefore, this paper proposes a novel
transformer-based trajectory prediction approach, specifically trained to
handle any observation length larger than one frame. We perform a comprehensive
evaluation of the proposed method using two large-scale highway trajectory
datasets, namely the highD and exiD. In addition, we study the impact of the
proposed prediction approach on motion planning and control tasks using
extensive merging scenarios from the exiD dataset. To the best of our
knowledge, this marks the first instance where such a large-scale highway
merging dataset has been employed for this purpose. The results demonstrate
that the prediction model achieves state-of-the-art performance on highD
dataset and maintains lower prediction error w.r.t. the constant velocity
across all observation lengths in exiD. Moreover, it significantly enhances
safety, comfort, and efficiency in dense traffic scenarios, as compared to the
constant velocity model.
|
[
"cs.RO",
"cs.LG"
] | false |
2306.05487
|
2023-06-08T18:17:48Z
|
Boosting with Tempered Exponential Measures
|
[
"Richard Nock",
"Ehsan Amid",
"Manfred K. Warmuth"
] |
One of the most popular ML algorithms, AdaBoost, can be derived from the dual
of a relative entropy minimization problem subject to the fact that the
positive weights on the examples sum to one. Essentially, harder examples
receive higher probabilities. We generalize this setup to the recently
introduced {\it tempered exponential measure}s (TEMs) where normalization is
enforced on a specific power of the measure and not the measure itself. TEMs
are indexed by a parameter $t$ and generalize exponential families ($t=1$). Our
algorithm, $t$-AdaBoost, recovers AdaBoost~as a special case ($t=1$). We show
that $t$-AdaBoost retains AdaBoost's celebrated exponential convergence rate
when $t\in [0,1)$ while allowing a slight improvement of the rate's hidden
constant compared to $t=1$. $t$-AdaBoost partially computes on a generalization
of classical arithmetic over the reals and brings notable properties like
guaranteed bounded leveraging coefficients for $t\in [0,1)$. From the loss that
$t$-AdaBoost minimizes (a generalization of the exponential loss), we show how
to derive a new family of {\it tempered} losses for the induction of
domain-partitioning classifiers like decision trees. Crucially, strict
properness is ensured for all while their boosting rates span the full known
spectrum. Experiments using $t$-AdaBoost+trees display that significant
leverage can be achieved by tuning $t$.
|
[
"cs.LG",
"stat.ML",
"I.2.6"
] | false |
2306.05528
|
2023-06-08T19:56:32Z
|
A brief review of contrastive learning applied to astrophysics
|
[
"Marc Huertas-Company",
"Regina Sarmiento",
"Johan Knapen"
] |
Reliable tools to extract patterns from high-dimensionality spaces are
becoming more necessary as astronomical datasets increase both in volume and
complexity. Contrastive Learning is a self-supervised machine learning
algorithm that extracts informative measurements from multi-dimensional
datasets, which has become increasingly popular in the computer vision and
Machine Learning communities in recent years. To do so, it maximizes the
agreement between the information extracted from augmented versions of the same
input data, making the final representation invariant to the applied
transformations. Contrastive Learning is particularly useful in astronomy for
removing known instrumental effects and for performing supervised
classifications and regressions with a limited amount of available labels,
showing a promising avenue towards \emph{Foundation Models}. This short review
paper briefly summarizes the main concepts behind contrastive learning and
reviews the first promising applications to astronomy. We include some
practical recommendations on which applications are particularly attractive for
contrastive learning.
|
[
"astro-ph.IM",
"cs.LG"
] | false |
2306.05545
|
2023-06-08T20:31:14Z
|
AI Enhanced Control Engineering Methods
|
[
"Ion Matei",
"Raj Minhas",
"Johan de Kleer",
"Alexander Felman"
] |
AI and machine learning based approaches are becoming ubiquitous in almost
all engineering fields. Control engineering cannot escape this trend. In this
paper, we explore how AI tools can be useful in control applications. The core
tool we focus on is automatic differentiation. Two immediate applications are
linearization of system dynamics for local stability analysis or for state
estimation using Kalman filters. We also explore other usages such as
conversion of differential algebraic equations to ordinary differential
equations for control design. In addition, we explore the use of machine
learning models for global parameterizations of state vectors and control
inputs in model predictive control applications. For each considered use case,
we give examples and results.
|
[
"math.OC",
"cs.LG"
] | false |
2306.06129
|
2023-06-08T10:12:50Z
|
Energy-efficient Wearable-to-Mobile Offload of ML Inference for
PPG-based Heart-Rate Estimation
|
[
"Alessio Burrello",
"Matteo Risso",
"Noemi Tomasello",
"Yukai Chen",
"Luca Benini",
"Enrico Macii",
"Massimo Poncino",
"Daniele Jahier Pagliari"
] |
Modern smartwatches often include photoplethysmographic (PPG) sensors to
measure heartbeats or blood pressure through complex algorithms that fuse PPG
data with other signals. In this work, we propose a collaborative inference
approach that uses both a smartwatch and a connected smartphone to maximize the
performance of heart rate (HR) tracking while also maximizing the smartwatch's
battery life. In particular, we first analyze the trade-offs between running
on-device HR tracking or offloading the work to the mobile. Then, thanks to an
additional step to evaluate the difficulty of the upcoming HR prediction, we
demonstrate that we can smartly manage the workload between smartwatch and
smartphone, maintaining a low mean absolute error (MAE) while reducing energy
consumption. We benchmark our approach on a custom smartwatch prototype,
including the STM32WB55 MCU and Bluetooth Low-Energy (BLE) communication, and a
Raspberry Pi3 as a proxy for the smartphone. With our Collaborative Heart Rate
Inference System (CHRIS), we obtain a set of Pareto-optimal configurations
demonstrating the same MAE as State-of-Art (SoA) algorithms while consuming
less energy. For instance, we can achieve approximately the same MAE of
TimePPG-Small (5.54 BPM MAE vs. 5.60 BPM MAE) while reducing the energy by
2.03x, with a configuration that offloads 80\% of the predictions to the phone.
Furthermore, accepting a performance degradation to 7.16 BPM of MAE, we can
achieve an energy consumption of 179 uJ per prediction, 3.03x less than running
TimePPG-Small on the smartwatch, and 1.82x less than streaming all the input
data to the phone.
|
[
"eess.SP",
"cs.LG"
] | false |
2306.06134
|
2023-06-08T19:58:30Z
|
Sound Explanation for Trustworthy Machine Learning
|
[
"Kai Jia",
"Pasapol Saowakon",
"Limor Appelbaum",
"Martin Rinard"
] |
We take a formal approach to the explainability problem of machine learning
systems. We argue against the practice of interpreting black-box models via
attributing scores to input components due to inherently conflicting goals of
attribution-based interpretation. We prove that no attribution algorithm
satisfies specificity, additivity, completeness, and baseline invariance. We
then formalize the concept, sound explanation, that has been informally adopted
in prior work. A sound explanation entails providing sufficient information to
causally explain the predictions made by a system. Finally, we present the
application of feature selection as a sound explanation for cancer prediction
models to cultivate trust among clinicians.
|
[
"cs.LG",
"cs.AI"
] | false |
2306.04884
|
2023-06-08T02:29:37Z
|
Faster Approximation Algorithms for Parameterized Graph Clustering and
Edge Labeling
|
[
"Vedangi Bengali",
"Nate Veldt"
] |
Graph clustering is a fundamental task in network analysis where the goal is
to detect sets of nodes that are well-connected to each other but sparsely
connected to the rest of the graph. We present faster approximation algorithms
for an NP-hard parameterized clustering framework called LambdaCC, which is
governed by a tunable resolution parameter and generalizes many other
clustering objectives such as modularity, sparsest cut, and cluster deletion.
Previous LambdaCC algorithms are either heuristics with no approximation
guarantees, or computationally expensive approximation algorithms. We provide
fast new approximation algorithms that can be made purely combinatorial. These
rely on a new parameterized edge labeling problem we introduce that generalizes
previous edge labeling problems that are based on the principle of strong
triadic closure and are of independent interest in social network analysis. Our
methods are orders of magnitude more scalable than previous approximation
algorithms and our lower bounds allow us to obtain a posteriori approximation
guarantees for previous heuristics that have no approximation guarantees of
their own.
|
[
"cs.DS",
"cs.DM",
"cs.LG",
"cs.SI"
] | false |
2306.04956
|
2023-06-08T06:06:42Z
|
Adaptive Fake Audio Detection with Low-Rank Model Squeezing
|
[
"Xiaohui Zhang",
"Jiangyan Yi",
"Jianhua Tao",
"Chenlong Wang",
"Le Xu",
"Ruibo Fu"
] |
The rapid advancement of spoofing algorithms necessitates the development of
robust detection methods capable of accurately identifying emerging fake audio.
Traditional approaches, such as finetuning on new datasets containing these
novel spoofing algorithms, are computationally intensive and pose a risk of
impairing the acquired knowledge of known fake audio types. To address these
challenges, this paper proposes an innovative approach that mitigates the
limitations associated with finetuning. We introduce the concept of training
low-rank adaptation matrices tailored specifically to the newly emerging fake
audio types. During the inference stage, these adaptation matrices are combined
with the existing model to generate the final prediction output. Extensive
experimentation is conducted to evaluate the efficacy of the proposed method.
The results demonstrate that our approach effectively preserves the prediction
accuracy of the existing model for known fake audio types. Furthermore, our
approach offers several advantages, including reduced storage memory
requirements and lower equal error rates compared to conventional finetuning
methods, particularly on specific spoofing algorithms.
|
[
"cs.SD",
"cs.LG",
"eess.AS"
] | false |
2306.05041
|
2023-06-08T08:49:42Z
|
Energy-Efficient Downlink Semantic Generative Communication with
Text-to-Image Generators
|
[
"Hyein Lee",
"Jihong Park",
"Sooyoung Kim",
"Jinho Choi"
] |
In this paper, we introduce a novel semantic generative communication (SGC)
framework, where generative users leverage text-to-image (T2I) generators to
create images locally from downloaded text prompts, while non-generative users
directly download images from a base station (BS). Although generative users
help reduce downlink transmission energy at the BS, they consume additional
energy for image generation and for uploading their generator state information
(GSI). We formulate the problem of minimizing the total energy consumption of
the BS and the users, and devise a generative user selection algorithm.
Simulation results corroborate that our proposed algorithm reduces total energy
by up to 54% compared to a baseline with all non-generative users.
|
[
"cs.LG",
"cs.AI",
"cs.DC"
] | false |
2306.05058
|
2023-06-08T09:23:09Z
|
Neuro-Symbolic Approaches for Context-Aware Human Activity Recognition
|
[
"Luca Arrotta",
"Gabriele Civitarese",
"Claudio Bettini"
] |
Deep Learning models are a standard solution for sensor-based Human Activity
Recognition (HAR), but their deployment is often limited by labeled data
scarcity and models' opacity. Neuro-Symbolic AI (NeSy) provides an interesting
research direction to mitigate these issues by infusing knowledge about context
information into HAR deep learning classifiers. However, existing NeSy methods
for context-aware HAR require computationally expensive symbolic reasoners
during classification, making them less suitable for deployment on
resource-constrained devices (e.g., mobile devices). Additionally, NeSy
approaches for context-aware HAR have never been evaluated on in-the-wild
datasets, and their generalization capabilities in real-world scenarios are
questionable. In this work, we propose a novel approach based on a semantic
loss function that infuses knowledge constraints in the HAR model during the
training phase, avoiding symbolic reasoning during classification. Our results
on scripted and in-the-wild datasets show the impact of different semantic loss
functions in outperforming a purely data-driven model. We also compare our
solution with existing NeSy methods and analyze each approach's strengths and
weaknesses. Our semantic loss remains the only NeSy solution that can be
deployed as a single DNN without the need for symbolic reasoning modules,
reaching recognition rates close (and better in some cases) to existing
approaches.
|
[
"cs.LG",
"cs.AI",
"eess.SP"
] | false |
2306.05066
|
2023-06-08T09:31:18Z
|
Causal Fairness for Outcome Control
|
[
"Drago Plecko",
"Elias Bareinboim"
] |
As society transitions towards an AI-based decision-making infrastructure, an
ever-increasing number of decisions once under control of humans are now
delegated to automated systems. Even though such developments make various
parts of society more efficient, a large body of evidence suggests that a great
deal of care needs to be taken to make such automated decision-making systems
fair and equitable, namely, taking into account sensitive attributes such as
gender, race, and religion. In this paper, we study a specific decision-making
task called outcome control in which an automated system aims to optimize an
outcome variable $Y$ while being fair and equitable. The interest in such a
setting ranges from interventions related to criminal justice and welfare, all
the way to clinical decision-making and public health. In this paper, we first
analyze through causal lenses the notion of benefit, which captures how much a
specific individual would benefit from a positive decision, counterfactually
speaking, when contrasted with an alternative, negative one. We introduce the
notion of benefit fairness, which can be seen as the minimal fairness
requirement in decision-making, and develop an algorithm for satisfying it. We
then note that the benefit itself may be influenced by the protected attribute,
and propose causal tools which can be used to analyze this. Finally, if some of
the variations of the protected attribute in the benefit are considered as
discriminatory, the notion of benefit fairness may need to be strengthened,
which leads us to articulating a notion of causal benefit fairness. Using this
notion, we develop a new optimization procedure capable of maximizing $Y$ while
ascertaining causal fairness in the decision process.
|
[
"cs.AI",
"cs.LG",
"stat.ML"
] | false |
2306.05068
|
2023-06-08T09:34:20Z
|
Shedding light on underrepresentation and Sampling Bias in machine
learning
|
[
"Sami Zhioua",
"Rūta Binkytė"
] |
Accurately measuring discrimination is crucial to faithfully assessing
fairness of trained machine learning (ML) models. Any bias in measuring
discrimination leads to either amplification or underestimation of the existing
disparity. Several sources of bias exist and it is assumed that bias resulting
from machine learning is born equally by different groups (e.g. females vs
males, whites vs blacks, etc.). If, however, bias is born differently by
different groups, it may exacerbate discrimination against specific
sub-populations. Sampling bias, is inconsistently used in the literature to
describe bias due to the sampling procedure. In this paper, we attempt to
disambiguate this term by introducing clearly defined variants of sampling
bias, namely, sample size bias (SSB) and underrepresentation bias (URB). We
show also how discrimination can be decomposed into variance, bias, and noise.
Finally, we challenge the commonly accepted mitigation approach that
discrimination can be addressed by collecting more samples of the
underrepresented group.
|
[
"cs.LG",
"cs.AI",
"cs.CY"
] | false |
2306.05071
|
2023-06-08T09:40:28Z
|
A Causal Framework for Decomposing Spurious Variations
|
[
"Drago Plecko",
"Elias Bareinboim"
] |
One of the fundamental challenges found throughout the data sciences is to
explain why things happen in specific ways, or through which mechanisms a
certain variable $X$ exerts influences over another variable $Y$. In statistics
and machine learning, significant efforts have been put into developing
machinery to estimate correlations across variables efficiently. In causal
inference, a large body of literature is concerned with the decomposition of
causal effects under the rubric of mediation analysis. However, many variations
are spurious in nature, including different phenomena throughout the applied
sciences. Despite the statistical power to estimate correlations and the
identification power to decompose causal effects, there is still little
understanding of the properties of spurious associations and how they can be
decomposed in terms of the underlying causal mechanisms. In this manuscript, we
develop formal tools for decomposing spurious variations in both Markovian and
Semi-Markovian models. We prove the first results that allow a non-parametric
decomposition of spurious effects and provide sufficient conditions for the
identification of such decompositions. The described approach has several
applications, ranging from explainable and fair AI to questions in epidemiology
and medicine, and we empirically demonstrate its use on a real-world dataset.
|
[
"stat.ME",
"cs.AI",
"cs.LG",
"stat.ML"
] | false |
2306.05100
|
2023-06-08T10:58:46Z
|
Communication-Efficient Gradient Descent-Accent Methods for Distributed
Variational Inequalities: Unified Analysis and Local Updates
|
[
"Siqi Zhang",
"Sayantan Choudhury",
"Sebastian U Stich",
"Nicolas Loizou"
] |
Distributed and federated learning algorithms and techniques associated
primarily with minimization problems. However, with the increase of minimax
optimization and variational inequality problems in machine learning, the
necessity of designing efficient distributed/federated learning approaches for
these problems is becoming more apparent. In this paper, we provide a unified
convergence analysis of communication-efficient local training methods for
distributed variational inequality problems (VIPs). Our approach is based on a
general key assumption on the stochastic estimates that allows us to propose
and analyze several novel local training algorithms under a single framework
for solving a class of structured non-monotone VIPs. We present the first local
gradient descent-accent algorithms with provable improved communication
complexity for solving distributed variational inequalities on heterogeneous
data. The general algorithmic framework recovers state-of-the-art algorithms
and their sharp convergence guarantees when the setting is specialized to
minimization or minimax optimization problems. Finally, we demonstrate the
strong performance of the proposed algorithms compared to state-of-the-art
methods when solving federated minimax optimization problems.
|
[
"math.OC",
"cs.LG",
"stat.ML"
] | false |
2306.05245
|
2023-06-08T14:44:23Z
|
Matching Latent Encoding for Audio-Text based Keyword Spotting
|
[
"Kumari Nishu",
"Minsik Cho",
"Devang Naik"
] |
Using audio and text embeddings jointly for Keyword Spotting (KWS) has shown
high-quality results, but the key challenge of how to semantically align two
embeddings for multi-word keywords of different sequence lengths remains
largely unsolved. In this paper, we propose an audio-text-based end-to-end
model architecture for flexible keyword spotting (KWS), which builds upon
learned audio and text embeddings. Our architecture uses a novel dynamic
programming-based algorithm, Dynamic Sequence Partitioning (DSP), to optimally
partition the audio sequence into the same length as the word-based text
sequence using the monotonic alignment of spoken content. Our proposed model
consists of an encoder block to get audio and text embeddings, a projector
block to project individual embeddings to a common latent space, and an
audio-text aligner containing a novel DSP algorithm, which aligns the audio and
text embeddings to determine if the spoken content is the same as the text.
Experimental results show that our DSP is more effective than other
partitioning schemes, and the proposed architecture outperformed the
state-of-the-art results on the public dataset in terms of Area Under the ROC
Curve (AUC) and Equal-Error-Rate (EER) by 14.4 % and 28.9%, respectively.
|
[
"eess.AS",
"cs.LG",
"cs.SD"
] | false |
2306.05261
|
2023-06-08T15:02:04Z
|
Representing and Learning Functions Invariant Under Crystallographic
Groups
|
[
"Ryan P. Adams",
"Peter Orbanz"
] |
Crystallographic groups describe the symmetries of crystals and other
repetitive structures encountered in nature and the sciences. These groups
include the wallpaper and space groups. We derive linear and nonlinear
representations of functions that are (1) smooth and (2) invariant under such a
group. The linear representation generalizes the Fourier basis to
crystallographically invariant basis functions. We show that such a basis
exists for each crystallographic group, that it is orthonormal in the relevant
$L_2$ space, and recover the standard Fourier basis as a special case for pure
shift groups. The nonlinear representation embeds the orbit space of the group
into a finite-dimensional Euclidean space. We show that such an embedding
exists for every crystallographic group, and that it factors functions through
a generalization of a manifold called an orbifold. We describe algorithms that,
given a standardized description of the group, compute the Fourier basis and an
embedding map. As examples, we construct crystallographically invariant neural
networks, kernel machines, and Gaussian processes.
|
[
"stat.ML",
"cond-mat.mtrl-sci",
"cs.LG"
] | false |
2306.05321
|
2023-06-08T16:13:29Z
|
Real-time whole-heart electromechanical simulations using Latent Neural
Ordinary Differential Equations
|
[
"Matteo Salvador",
"Marina Strocchi",
"Francesco Regazzoni",
"Luca Dede'",
"Steven Niederer",
"Alfio Quarteroni"
] |
Cardiac digital twins provide a physics and physiology informed framework to
deliver predictive and personalized medicine. However, high-fidelity
multi-scale cardiac models remain a barrier to adoption due to their extensive
computational costs and the high number of model evaluations needed for
patient-specific personalization. Artificial Intelligence-based methods can
make the creation of fast and accurate whole-heart digital twins feasible. In
this work, we use Latent Neural Ordinary Differential Equations (LNODEs) to
learn the temporal pressure-volume dynamics of a heart failure patient. Our
surrogate model based on LNODEs is trained from 400 3D-0D whole-heart
closed-loop electromechanical simulations while accounting for 43 model
parameters, describing single cell through to whole organ and cardiovascular
hemodynamics. The trained LNODEs provides a compact and efficient
representation of the 3D-0D model in a latent space by means of a feedforward
fully-connected Artificial Neural Network that retains 3 hidden layers with 13
neurons per layer and allows for 300x real-time numerical simulations of the
cardiac function on a single processor of a standard laptop. This surrogate
model is employed to perform global sensitivity analysis and robust parameter
estimation with uncertainty quantification in 3 hours of computations, still on
a single processor. We match pressure and volume time traces unseen by the
LNODEs during the training phase and we calibrate 4 to 11 model parameters
while also providing their posterior distribution. This paper introduces the
most advanced surrogate model of cardiac function available in the literature
and opens new important venues for parameter calibration in cardiac digital
twins.
|
[
"math.NA",
"cs.LG",
"cs.NA"
] | false |
2306.05445
|
2023-06-08T17:12:08Z
|
Towards Predicting Equilibrium Distributions for Molecular Systems with
Deep Learning
|
[
"Shuxin Zheng",
"Jiyan He",
"Chang Liu",
"Yu Shi",
"Ziheng Lu",
"Weitao Feng",
"Fusong Ju",
"Jiaxi Wang",
"Jianwei Zhu",
"Yaosen Min",
"He Zhang",
"Shidi Tang",
"Hongxia Hao",
"Peiran Jin",
"Chi Chen",
"Frank Noé",
"Haiguang Liu",
"Tie-Yan Liu"
] |
Advances in deep learning have greatly improved structure prediction of
molecules. However, many macroscopic observations that are important for
real-world applications are not functions of a single molecular structure, but
rather determined from the equilibrium distribution of structures. Traditional
methods for obtaining these distributions, such as molecular dynamics
simulation, are computationally expensive and often intractable. In this paper,
we introduce a novel deep learning framework, called Distributional Graphormer
(DiG), in an attempt to predict the equilibrium distribution of molecular
systems. Inspired by the annealing process in thermodynamics, DiG employs deep
neural networks to transform a simple distribution towards the equilibrium
distribution, conditioned on a descriptor of a molecular system, such as a
chemical graph or a protein sequence. This framework enables efficient
generation of diverse conformations and provides estimations of state
densities. We demonstrate the performance of DiG on several molecular tasks,
including protein conformation sampling, ligand structure sampling,
catalyst-adsorbate sampling, and property-guided structure generation. DiG
presents a significant advancement in methodology for statistically
understanding molecular systems, opening up new research opportunities in
molecular science.
|
[
"physics.chem-ph",
"cs.LG",
"q-bio.BM"
] | false |
2306.05484
|
2023-06-08T18:10:37Z
|
Task-specific experimental design for treatment effect estimation
|
[
"Bethany Connolly",
"Kim Moore",
"Tobias Schwedes",
"Alexander Adam",
"Gary Willis",
"Ilya Feige",
"Christopher Frye"
] |
Understanding causality should be a core requirement of any attempt to build
real impact through AI. Due to the inherent unobservability of counterfactuals,
large randomised trials (RCTs) are the standard for causal inference. But large
experiments are generically expensive, and randomisation carries its own costs,
e.g. when suboptimal decisions are trialed. Recent work has proposed more
sample-efficient alternatives to RCTs, but these are not adaptable to the
downstream application for which the causal effect is sought. In this work, we
develop a task-specific approach to experimental design and derive sampling
strategies customised to particular downstream applications. Across a range of
important tasks, real-world datasets, and sample sizes, our method outperforms
other benchmarks, e.g. requiring an order-of-magnitude less data to match RCT
performance on targeted marketing tasks.
|
[
"stat.ME",
"cs.LG",
"stat.ML"
] | false |
2306.05497
|
2023-06-08T18:38:55Z
|
Reevaluating Loss Functions: Enhancing Robustness to Label Noise in Deep
Learning Models
|
[
"Max Staats",
"Matthias Thamm",
"Bernd Rosenow"
] |
Large annotated datasets inevitably contain incorrect labels, which poses a
major challenge for the training of deep neural networks as they easily fit the
labels. Only when training with a robust model that is not easily distracted by
the noise, a good generalization performance can be achieved. A simple yet
effective way to create a noise robust model is to use a noise robust loss
function. However, the number of proposed loss functions is large, they often
come with hyperparameters, and may learn slower than the widely used but noise
sensitive Cross Entropy loss. By heuristic considerations and extensive
numerical experiments, we study in which situations the proposed loss functions
are applicable and give suggestions on how to choose an appropriate loss.
Additionally, we propose a novel technique to enhance learning with bounded
loss functions: the inclusion of an output bias, i.e. a slight increase in the
neuron pre-activation corresponding to the correct label. Surprisingly, we find
that this not only significantly improves the learning of bounded losses, but
also leads to the Mean Absolute Error loss outperforming the Cross Entropy loss
on the Cifar-100 dataset - even in the absence of additional label noise. This
suggests that training with a bounded loss function can be advantageous even in
the presence of minimal label noise. To further strengthen our analysis of the
learning behavior of different loss functions, we additionally design and test
a novel loss function denoted as Bounded Cross Entropy.
|
[
"cs.LG",
"cond-mat.dis-nn",
"cs.AI"
] | false |
2306.05567
|
2023-06-08T21:19:42Z
|
Intelligent Energy Management with IoT Framework in Smart Cities Using
Intelligent Analysis: An Application of Machine Learning Methods for Complex
Networks and Systems
|
[
"Maryam Nikpour",
"Parisa Behvand Yousefi",
"Hadi Jafarzadeh",
"Kasra Danesh",
"Mohsen Ahmadi"
] |
Smart buildings are increasingly using Internet of Things (IoT)-based
wireless sensing systems to reduce their energy consumption and environmental
impact. As a result of their compact size and ability to sense, measure, and
compute all electrical properties, Internet of Things devices have become
increasingly important in our society. A major contribution of this study is
the development of a comprehensive IoT-based framework for smart city energy
management, incorporating multiple components of IoT architecture and
framework. An IoT framework for intelligent energy management applications that
employ intelligent analysis is an essential system component that collects and
stores information. Additionally, it serves as a platform for the development
of applications by other companies. Furthermore, we have studied intelligent
energy management solutions based on intelligent mechanisms. The depletion of
energy resources and the increase in energy demand have led to an increase in
energy consumption and building maintenance. The data collected is used to
monitor, control, and enhance the efficiency of the system.
|
[
"cs.LG",
"cs.CY",
"cs.SY",
"eess.SY"
] | false |
2306.06124
|
2023-06-08T04:41:34Z
|
Unsupervised clustering of disturbances in power systems via deep
convolutional autoencoders
|
[
"Md Maidul Islam",
"Md Omar Faruque",
"Joshua Butterfield",
"Gaurav Singh",
"Thomas A. Cooke"
] |
Power quality (PQ) events are recorded by PQ meters whenever anomalous events
are detected on the power grid. Using neural networks with machine learning can
aid in accurately classifying the recorded waveforms and help power system
engineers diagnose and rectify the root causes of problems. However, many of
the waveforms captured during a disturbance in the power system need to be
labeled for supervised learning, leaving a large number of data recordings for
engineers to process manually or go unseen. This paper presents an autoencoder
and K-means clustering-based unsupervised technique that can be used to cluster
PQ events into categories like sag, interruption, transients, normal, and
harmonic distortion to enable filtering of anomalous waveforms from recurring
or normal waveforms. The method is demonstrated using three-phase,
field-obtained voltage waveforms recorded in a distribution grid. First, a
convolutional autoencoder compresses the input signals into a set of lower
feature dimensions which, after further processing, is passed to the K-means
algorithm to identify data clusters. Using a small, labeled dataset, numerical
labels are then assigned to events based on a cosine similarity analysis.
Finally, the study analyzes the clusters using the t-distributed stochastic
neighbor embedding (t-SNE) visualization tool, demonstrating that the technique
can help investigate a large number of captured events in a quick manner.
|
[
"eess.SP",
"cs.AI",
"cs.LG",
"cs.SY"
] | false |
2306.13661
|
2023-06-08T13:04:44Z
|
Constructing Time-Series Momentum Portfolios with Deep Multi-Task
Learning
|
[
"Joel Ong",
"Dorien Herremans"
] |
A diversified risk-adjusted time-series momentum (TSMOM) portfolio can
deliver substantial abnormal returns and offer some degree of tail risk
protection during extreme market events. The performance of existing TSMOM
strategies, however, relies not only on the quality of the momentum signal but
also on the efficacy of the volatility estimator. Yet many of the existing
studies have always considered these two factors to be independent. Inspired by
recent progress in Multi-Task Learning (MTL), we present a new approach using
MTL in a deep neural network architecture that jointly learns portfolio
construction and various auxiliary tasks related to volatility, such as
forecasting realized volatility as measured by different volatility estimators.
Through backtesting from January 2000 to December 2020 on a diversified
portfolio of continuous futures contracts, we demonstrate that even after
accounting for transaction costs of up to 3 basis points, our approach
outperforms existing TSMOM strategies. Moreover, experiments confirm that
adding auxiliary tasks indeed boosts the portfolio's performance. These
findings demonstrate that MTL can be a powerful tool in finance.
|
[
"q-fin.CP",
"cs.LG",
"q-fin.PM"
] | false |
2306.14901
|
2023-06-08T08:53:03Z
|
Phonon dynamic behaviors induced by amorphous interlayer at
heterointerfaces
|
[
"Quanjie Wang",
"Jie Zhang",
"Vladimir Chernysh",
"Xiangjun Liu"
] |
Interface impedes heat flow in heterostructures and the interfacial thermal
resistance (ITR) has become a critical issue for thermal dissipation in
electronic devices. To explore the mechanism leading to the ITR, in this work,
the dynamic behaviors of phonons passing through the GaN/AlN interface with an
amorphous interlayer is investigated by using phonon wave packet simulation. It
is found the amorphous interlayer significantly impedes phonon transport across
the interface, and leads to remarkable phonon mode conversions, such as
LA$\rightarrow$TA, TA$\rightarrow$LA, and LA$\rightarrow$TO conversion.
However, due to mode conversion and inelastic scattering, we found a portion of
high-frequency TA phonons, which are higher than the cut-off frequency and
cannot transmit across the ideal sharp interface, can partially transmit across
the amorphous interlayer, which introduces additional thermal transport
channels through the interface and has positive effect on interfacial thermal
conductance. According to phonon transmission coefficient, it is found the ITR
increases with increasing of amorphous interlayer thickness L. The phonon
transmission coefficient exhibits an obvious oscillation behavior, which is
attributed to the multiple phonon scattering in the amorphous interlayer, and
the oscillation period is further revealed to be consistent with the
theoretical prediction by the two-beam interference equation. In addition,
obvious phonon frequency shifts and phonon energy localization phenomena were
observed in the amorphous interlayer. Finally, to improve phonon transmission,
the interface morphology was further optimized via the annealing reconstruction
technique, which results in re-crystallization of the amorphous interlayer and
the decrease of ITR by ~21% as L=2 nm.
|
[
"physics.app-ph",
"cs.LG",
"physics.comp-ph"
] | false |
2306.05255
|
2023-06-08T14:52:56Z
|
Toward more accurate and generalizable brain deformation estimators for
traumatic brain injury detection with unsupervised domain adaptation
|
[
"Xianghao Zhan",
"Jiawei Sun",
"Yuzhe Liu",
"Nicholas J. Cecchi",
"Enora Le Flao",
"Olivier Gevaert",
"Michael M. Zeineh",
"David B. Camarillo"
] |
Machine learning head models (MLHMs) are developed to estimate brain
deformation for early detection of traumatic brain injury (TBI). However, the
overfitting to simulated impacts and the lack of generalizability caused by
distributional shift of different head impact datasets hinders the broad
clinical applications of current MLHMs. We propose brain deformation estimators
that integrates unsupervised domain adaptation with a deep neural network to
predict whole-brain maximum principal strain (MPS) and MPS rate (MPSR). With
12,780 simulated head impacts, we performed unsupervised domain adaptation on
on-field head impacts from 302 college football (CF) impacts and 457 mixed
martial arts (MMA) impacts using domain regularized component analysis (DRCA)
and cycle-GAN-based methods. The new model improved the MPS/MPSR estimation
accuracy, with the DRCA method significantly outperforming other domain
adaptation methods in prediction accuracy (p<0.001): MPS RMSE: 0.027 (CF) and
0.037 (MMA); MPSR RMSE: 7.159 (CF) and 13.022 (MMA). On another two hold-out
test sets with 195 college football impacts and 260 boxing impacts, the DRCA
model significantly outperformed the baseline model without domain adaptation
in MPS and MPSR estimation accuracy (p<0.001). The DRCA domain adaptation
reduces the MPS/MPSR estimation error to be well below TBI thresholds, enabling
accurate brain deformation estimation to detect TBI in future clinical
applications.
|
[
"cs.LG",
"eess.SP",
"physics.bio-ph",
"q-bio.QM",
"stat.AP"
] | false |
2306.05363
|
2023-06-08T17:07:24Z
|
Subject clustering by IF-PCA and several recent methods
|
[
"Dieyi Chen",
"Jiashun Jin",
"Zheng Tracy Ke"
] |
Subject clustering (i.e., the use of measured features to cluster subjects,
such as patients or cells, into multiple groups) is a problem of great
interest. In recent years, many approaches were proposed, among which
unsupervised deep learning (UDL) has received a great deal of attention. Two
interesting questions are (a) how to combine the strengths of UDL and other
approaches, and (b) how these approaches compare to one other.
We combine Variational Auto-Encoder (VAE), a popular UDL approach, with the
recent idea of Influential Feature PCA (IF-PCA), and propose IF-VAE as a new
method for subject clustering. We study IF-VAE and compare it with several
other methods (including IF-PCA, VAE, Seurat, and SC3) on $10$ gene microarray
data sets and $8$ single-cell RNA-seq data sets. We find that IF-VAE
significantly improves over VAE, but still underperforms IF-PCA. We also find
that IF-PCA is quite competitive, which slightly outperforms Seurat and SC3
over the $8$ single-cell data sets. IF-PCA is conceptually simple and permits
delicate analysis. We demonstrate that IF-PCA is capable of achieving the phase
transition in a Rare/Weak model. Comparatively, Seurat and SC3 are more complex
and theoretically difficult to analyze (for these reasons, their optimality
remains unclear).
|
[
"stat.ME",
"cs.LG",
"math.ST",
"stat.AP",
"stat.TH"
] | false |
2306.05592
|
2023-06-08T23:38:25Z
|
Evaluating and Incentivizing Diverse Data Contributions in Collaborative
Learning
|
[
"Baihe Huang",
"Sai Praneeth Karimireddy",
"Michael I. Jordan"
] |
For a federated learning model to perform well, it is crucial to have a
diverse and representative dataset. However, the data contributors may only be
concerned with the performance on a specific subset of the population, which
may not reflect the diversity of the wider population. This creates a tension
between the principal (the FL platform designer) who cares about global
performance and the agents (the data collectors) who care about local
performance. In this work, we formulate this tension as a game between the
principal and multiple agents, and focus on the linear experiment design
problem to formally study their interaction. We show that the statistical
criterion used to quantify the diversity of the data, as well as the choice of
the federated learning algorithm used, has a significant effect on the
resulting equilibrium. We leverage this to design simple optimal federated
learning mechanisms that encourage data collectors to contribute data
representative of the global population, thereby maximizing global performance.
|
[
"cs.GT",
"cs.CY",
"cs.DC",
"cs.LG",
"econ.TH"
] | false |
2306.05612
|
2023-06-09T01:11:50Z
|
Spatial Re-parameterization for N:M Sparsity
|
[
"Yuxin Zhang",
"Mingbao Lin",
"Yunshan Zhong",
"Mengzhao Chen",
"Fei Chao",
"Rongrong Ji"
] |
This paper presents a Spatial Re-parameterization (SpRe) method for the N:M
sparsity in CNNs. SpRe is stemmed from an observation regarding the restricted
variety in spatial sparsity present in N:M sparsity compared with unstructured
sparsity. Particularly, N:M sparsity exhibits a fixed sparsity rate within the
spatial domains due to its distinctive pattern that mandates N non-zero
components among M successive weights in the input channel dimension of
convolution filters. On the contrary, we observe that unstructured sparsity
displays a substantial divergence in sparsity across the spatial domains, which
we experimentally verified to be very crucial for its robust performance
retention compared with N:M sparsity. Therefore, SpRe employs the
spatial-sparsity distribution of unstructured sparsity to assign an extra
branch in conjunction with the original N:M branch at training time, which
allows the N:M sparse network to sustain a similar distribution of spatial
sparsity with unstructured sparsity. During inference, the extra branch can be
further re-parameterized into the main N:M branch, without exerting any
distortion on the sparse pattern or additional computation costs. SpRe has
achieved a commendable feat by matching the performance of N:M sparsity methods
with state-of-the-art unstructured sparsity methods across various benchmarks.
Code and models are anonymously available at
\url{https://github.com/zyxxmu/SpRe}.
|
[
"cs.CV"
] | false |
2306.05663
|
2023-06-09T04:11:43Z
|
Improving LiDAR 3D Object Detection via Range-based Point Cloud Density
Optimization
|
[
"Eduardo R. Corral-Soto",
"Alaap Grandhi",
"Yannis Y. He",
"Mrigank Rochan",
"Bingbing Liu"
] |
In recent years, much progress has been made in LiDAR-based 3D object
detection mainly due to advances in detector architecture designs and
availability of large-scale LiDAR datasets. Existing 3D object detectors tend
to perform well on the point cloud regions closer to the LiDAR sensor as
opposed to on regions that are farther away. In this paper, we investigate this
problem from the data perspective instead of detector architecture design. We
observe that there is a learning bias in detection models towards the dense
objects near the sensor and show that the detection performance can be improved
by simply manipulating the input point cloud density at different distance
ranges without modifying the detector architecture and without data
augmentation. We propose a model-free point cloud density adjustment
pre-processing mechanism that uses iterative MCMC optimization to estimate
optimal parameters for altering the point density at different distance ranges.
We conduct experiments using four state-of-the-art LiDAR 3D object detectors on
two public LiDAR datasets, namely Waymo and ONCE. Our results demonstrate that
our range-based point cloud density manipulation technique can improve the
performance of the existing detectors, which in turn could potentially inspire
future detector designs.
|
[
"cs.CV"
] | false |
2306.05675
|
2023-06-09T05:19:51Z
|
Illumination Controllable Dehazing Network based on Unsupervised Retinex
Embedding
|
[
"Jie Gui",
"Xiaofeng Cong",
"Lei He",
"Yuan Yan Tang",
"James Tin-Yau Kwok"
] |
On the one hand, the dehazing task is an illposedness problem, which means
that no unique solution exists. On the other hand, the dehazing task should
take into account the subjective factor, which is to give the user selectable
dehazed images rather than a single result. Therefore, this paper proposes a
multi-output dehazing network by introducing illumination controllable ability,
called IC-Dehazing. The proposed IC-Dehazing can change the illumination
intensity by adjusting the factor of the illumination controllable module,
which is realized based on the interpretable Retinex theory. Moreover, the
backbone dehazing network of IC-Dehazing consists of a Transformer with double
decoders for high-quality image restoration. Further, the prior-based loss
function and unsupervised training strategy enable IC-Dehazing to complete the
parameter learning process without the need for paired data. To demonstrate the
effectiveness of the proposed IC-Dehazing, quantitative and qualitative
experiments are conducted on image dehazing, semantic segmentation, and object
detection tasks. Code is available at
https://github.com/Xiaofeng-life/ICDehazing.
|
[
"cs.CV"
] | false |
2306.05688
|
2023-06-09T06:00:05Z
|
ModeT: Learning Deformable Image Registration via Motion Decomposition
Transformer
|
[
"Haiqiao Wang",
"Dong Ni",
"Yi Wang"
] |
The Transformer structures have been widely used in computer vision and have
recently made an impact in the area of medical image registration. However, the
use of Transformer in most registration networks is straightforward. These
networks often merely use the attention mechanism to boost the feature learning
as the segmentation networks do, but do not sufficiently design to be adapted
for the registration task. In this paper, we propose a novel motion
decomposition Transformer (ModeT) to explicitly model multiple motion
modalities by fully exploiting the intrinsic capability of the Transformer
structure for deformation estimation. The proposed ModeT naturally transforms
the multi-head neighborhood attention relationship into the multi-coordinate
relationship to model multiple motion modes. Then the competitive weighting
module (CWM) fuses multiple deformation sub-fields to generate the resulting
deformation field. Extensive experiments on two public brain magnetic resonance
imaging (MRI) datasets show that our method outperforms current
state-of-the-art registration networks and Transformers, demonstrating the
potential of our ModeT for the challenging non-rigid deformation estimation
problem. The benchmarks and our code are publicly available at
https://github.com/ZAX130/SmileCode.
|
[
"cs.CV"
] | false |
2306.05689
|
2023-06-09T06:02:01Z
|
Single-Stage Visual Relationship Learning using Conditional Queries
|
[
"Alakh Desai",
"Tz-Ying Wu",
"Subarna Tripathi",
"Nuno Vasconcelos"
] |
Research in scene graph generation (SGG) usually considers two-stage models,
that is, detecting a set of entities, followed by combining them and labeling
all possible relationships. While showing promising results, the pipeline
structure induces large parameter and computation overhead, and typically
hinders end-to-end optimizations. To address this, recent research attempts to
train single-stage models that are computationally efficient. With the advent
of DETR, a set based detection model, one-stage models attempt to predict a set
of subject-predicate-object triplets directly in a single shot. However, SGG is
inherently a multi-task learning problem that requires modeling entity and
predicate distributions simultaneously. In this paper, we propose Transformers
with conditional queries for SGG, namely, TraCQ with a new formulation for SGG
that avoids the multi-task learning problem and the combinatorial entity pair
distribution. We employ a DETR-based encoder-decoder design and leverage
conditional queries to significantly reduce the entity label space as well,
which leads to 20% fewer parameters compared to state-of-the-art single-stage
models. Experimental results show that TraCQ not only outperforms existing
single-stage scene graph generation methods, it also beats many
state-of-the-art two-stage methods on the Visual Genome dataset, yet is capable
of end-to-end training and faster inference.
|
[
"cs.CV"
] | false |
2306.05691
|
2023-06-09T06:10:59Z
|
DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical
Flow
|
[
"Risheek Garrepalli",
"Jisoo Jeong",
"Rajeswaran C Ravindran",
"Jamie Menjay Lin",
"Fatih Porikli"
] |
Recent advancements in neural network-based optical flow estimation often
come with prohibitively high computational and memory requirements, presenting
challenges in their model adaptation for mobile and low-power use cases. In
this paper, we introduce a lightweight low-latency and memory-efficient model,
Dynamic Iterative Field Transforms (DIFT), for optical flow estimation feasible
for edge applications such as mobile, XR, micro UAVs, robotics and cameras.
DIFT follows an iterative refinement framework leveraging variable resolution
of cost volumes for correspondence estimation. We propose a memory efficient
solution for cost volume processing to reduce peak memory. Also, we present a
novel dynamic coarse-to-fine cost volume processing during various stages of
refinement to avoid multiple levels of cost volumes. We demonstrate first
real-time cost-volume based optical flow DL architecture on Snapdragon 8 Gen 1
HTP efficient mobile AI accelerator with 32 inf/sec and 5.89 EPE (endpoint
error) on KITTI with manageable accuracy-performance tradeoffs.
|
[
"cs.CV"
] | false |
2306.05978
|
2023-06-09T15:45:23Z
|
3D objects and scenes classification, recognition, segmentation, and
reconstruction using 3D point cloud data: A review
|
[
"Omar Elharrouss",
"Kawther Hassine",
"Ayman Zayyan",
"Zakariyae Chatri",
"Noor almaadeed",
"Somaya Al-Maadeed",
"Khalid Abualsaud"
] |
Three-dimensional (3D) point cloud analysis has become one of the attractive
subjects in realistic imaging and machine visions due to its simplicity,
flexibility and powerful capacity of visualization. Actually, the
representation of scenes and buildings using 3D shapes and formats leveraged
many applications among which automatic driving, scenes and objects
reconstruction, etc. Nevertheless, working with this emerging type of data has
been a challenging task for objects representation, scenes recognition,
segmentation, and reconstruction. In this regard, a significant effort has
recently been devoted to developing novel strategies, using different
techniques such as deep learning models. To that end, we present in this paper
a comprehensive review of existing tasks on 3D point cloud: a well-defined
taxonomy of existing techniques is performed based on the nature of the adopted
algorithms, application scenarios, and main objectives. Various tasks performed
on 3D point could data are investigated, including objects and scenes
detection, recognition, segmentation and reconstruction. In addition, we
introduce a list of used datasets, we discuss respective evaluation metrics and
we compare the performance of existing solutions to better inform the
state-of-the-art and identify their limitations and strengths. Lastly, we
elaborate on current challenges facing the subject of technology and future
trends attracting considerable interest, which could be a starting point for
upcoming research studies
|
[
"cs.CV"
] | false |
2306.05985
|
2023-06-09T15:53:01Z
|
Beyond Detection: Visual Realism Assessment of Deepfakes
|
[
"Luka Dragar",
"Peter Peer",
"Vitomir Štruc",
"Borut Batagelj"
] |
In the era of rapid digitalization and artificial intelligence advancements,
the development of DeepFake technology has posed significant security and
privacy concerns. This paper presents an effective measure to assess the visual
realism of DeepFake videos. We utilize an ensemble of two Convolutional Neural
Network (CNN) models: Eva and ConvNext. These models have been trained on the
DeepFake Game Competition (DFGC) 2022 dataset and aim to predict Mean Opinion
Scores (MOS) from DeepFake videos based on features extracted from sequences of
frames. Our method secured the third place in the recent DFGC on Visual Realism
Assessment held in conjunction with the 2023 International Joint Conference on
Biometrics (IJCB 2023). We provide an over\-view of the models, data
preprocessing, and training procedures. We also report the performance of our
models against the competition's baseline model and discuss the implications of
our findings.
|
[
"cs.CV"
] | false |
2306.06089
|
2023-06-09T17:51:20Z
|
Computational Flash Photography through Intrinsics
|
[
"Sepideh Sarajian Maralan",
"Chris Careaga",
"Yağız Aksoy"
] |
Flash is an essential tool as it often serves as the sole controllable light
source in everyday photography. However, the use of flash is a binary decision
at the time a photograph is captured with limited control over its
characteristics such as strength or color. In this work, we study the
computational control of the flash light in photographs taken with or without
flash. We present a physically motivated intrinsic formulation for flash
photograph formation and develop flash decomposition and generation methods for
flash and no-flash photographs, respectively. We demonstrate that our intrinsic
formulation outperforms alternatives in the literature and allows us to
computationally control flash in in-the-wild images.
|
[
"cs.CV"
] | false |
2306.06092
|
2023-06-09T17:52:34Z
|
Realistic Saliency Guided Image Enhancement
|
[
"S. Mahdi H. Miangoleh",
"Zoya Bylinskii",
"Eric Kee",
"Eli Shechtman",
"Yağız Aksoy"
] |
Common editing operations performed by professional photographers include the
cleanup operations: de-emphasizing distracting elements and enhancing subjects.
These edits are challenging, requiring a delicate balance between manipulating
the viewer's attention while maintaining photo realism. While recent approaches
can boast successful examples of attention attenuation or amplification, most
of them also suffer from frequent unrealistic edits. We propose a realism loss
for saliency-guided image enhancement to maintain high realism across varying
image types, while attenuating distractors and amplifying objects of interest.
Evaluations with professional photographers confirm that we achieve the dual
objective of realism and effectiveness, and outperform the recent approaches on
their own datasets, while requiring a smaller memory footprint and runtime. We
thus offer a viable solution for automating image enhancement and photo cleanup
operations.
|
[
"cs.CV"
] | true |
2306.06288
|
2023-06-09T22:29:42Z
|
SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remote Sensing
Image Dehazing Using Satellite-to-Ground NDVI Knowledge
|
[
"Zepeng Liu",
"Zhicheng Yang",
"Mingye Zhu",
"Andy Wong",
"Yibing Wei",
"Mei Han",
"Jun Yu",
"Jui-Hsin Lai"
] |
Image dehazing is a meaningful low-level computer vision task and can be
applied to a variety of contexts. In our industrial deployment scenario based
on remote sensing (RS) images, the quality of image dehazing directly affects
the grade of our crop identification and growth monitoring products. However,
the widely used peak signal-to-noise ratio (PSNR) and structural similarity
index (SSIM) provide ambiguous visual interpretation. In this paper, we design
a new objective metric for RS image dehazing evaluation. Our proposed metric
leverages a ground-based phenology observation resource to calculate the
vegetation index error between RS and ground images at a hazy date. Extensive
experiments validate that our metric appropriately evaluates different dehazing
models and is in line with human visual perception.
|
[
"cs.CV"
] | false |
2306.05869
|
2023-06-09T13:02:31Z
|
Leaving the Lines Behind: Vision-Based Crop Row Exit for Agricultural
Robot Navigation
|
[
"Rajitha de Silva",
"Grzegorz Cielniak",
"Junfeng Gao"
] |
Usage of purely vision based solutions for row switching is not well explored
in existing vision based crop row navigation frameworks. This method only uses
RGB images for local feature matching based visual feedback to exit crop row.
Depth images were used at crop row end to estimate the navigation distance
within headland. The algorithm was tested on diverse headland areas with soil
and vegetation. The proposed method could reach the end of the crop row and
then navigate into the headland completely leaving behind the crop row with an
error margin of 50 cm.
|
[
"cs.RO",
"cs.CV"
] | false |
2306.05912
|
2023-06-09T14:06:26Z
|
Single-Image-Based Deep Learning for Segmentation of Early Esophageal
Cancer Lesions
|
[
"Haipeng Li",
"Dingrui Liu",
"Yu Zeng",
"Shuaicheng Liu",
"Tao Gan",
"Nini Rao",
"Jinlin Yang",
"Bing Zeng"
] |
Accurate segmentation of lesions is crucial for diagnosis and treatment of
early esophageal cancer (EEC). However, neither traditional nor deep
learning-based methods up to today can meet the clinical requirements, with the
mean Dice score - the most important metric in medical image analysis - hardly
exceeding 0.75. In this paper, we present a novel deep learning approach for
segmenting EEC lesions. Our approach stands out for its uniqueness, as it
relies solely on a single image coming from one patient, forming the so-called
"You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network"
learning ensures complete patient privacy as it does not use any images from
other patients as the training data. On the other hand, it avoids nearly all
generalization-related problems since each trained network is applied only to
the input image itself. In particular, we can push the training to
"over-fitting" as much as possible to increase the segmentation accuracy. Our
technical details include an interaction with clinical physicians to utilize
their expertise, a geometry-based rendering of a single lesion image to
generate the training set (the \emph{biggest} novelty), and an edge-enhanced
UNet. We have evaluated YOHO over an EEC data-set created by ourselves and
achieved a mean Dice score of 0.888, which represents a significant advance
toward clinical applications.
|
[
"eess.IV",
"cs.CV"
] | false |
2306.06149
|
2023-06-09T12:23:20Z
|
Read, look and detect: Bounding box annotation from image-caption pairs
|
[
"Eduardo Hugo Sanchez"
] |
Various methods have been proposed to detect objects while reducing the cost
of data annotation. For instance, weakly supervised object detection (WSOD)
methods rely only on image-level annotations during training. Unfortunately,
data annotation remains expensive since annotators must provide the categories
describing the content of each image and labeling is restricted to a fixed set
of categories. In this paper, we propose a method to locate and label objects
in an image by using a form of weaker supervision: image-caption pairs. By
leveraging recent advances in vision-language (VL) models and self-supervised
vision transformers (ViTs), our method is able to perform phrase grounding and
object detection in a weakly supervised manner. Our experiments demonstrate the
effectiveness of our approach by achieving a 47.51% recall@1 score in phrase
grounding on Flickr30k Entities and establishing a new state-of-the-art in
object detection by achieving 21.1 mAP 50 and 10.5 mAP 50:95 on MS COCO when
exclusively relying on image-caption pairs.
|
[
"cs.CV",
"cs.AI"
] | false |
2306.06212
|
2023-06-09T19:24:39Z
|
Aladdin: Zero-Shot Hallucination of Stylized 3D Assets from Abstract
Scene Descriptions
|
[
"Ian Huang",
"Vrishab Krishna",
"Omoruyi Atekha",
"Leonidas Guibas"
] |
What constitutes the "vibe" of a particular scene? What should one find in "a
busy, dirty city street", "an idyllic countryside", or "a crime scene in an
abandoned living room"? The translation from abstract scene descriptions to
stylized scene elements cannot be done with any generality by extant systems
trained on rigid and limited indoor datasets. In this paper, we propose to
leverage the knowledge captured by foundation models to accomplish this
translation. We present a system that can serve as a tool to generate stylized
assets for 3D scenes described by a short phrase, without the need to enumerate
the objects to be found within the scene or give instructions on their
appearance. Additionally, it is robust to open-world concepts in a way that
traditional methods trained on limited data are not, affording more creative
freedom to the 3D artist. Our system demonstrates this using a foundation model
"team" composed of a large language model, a vision-language model and several
image diffusion models, which communicate using an interpretable and
user-editable intermediate representation, thus allowing for more versatile and
controllable stylized asset generation for 3D artists. We introduce novel
metrics for this task, and show through human evaluations that in 91% of the
cases, our system outputs are judged more faithful to the semantics of the
input scene description than the baseline, thus highlighting the potential of
this approach to radically accelerate the 3D content creation process for 3D
artists.
|
[
"cs.CV",
"cs.GR"
] | true |
2306.06217
|
2023-06-09T19:30:49Z
|
BioGAN: An unpaired GAN-based image to image translation model for
microbiological images
|
[
"Saber Mirzaee Bafti",
"Chee Siang Ang",
"Gianluca Marcelli",
"Md. Moinul Hossain",
"Sadiya Maxamhud",
"Anastasios D. Tsaousis"
] |
A diversified dataset is crucial for training a well-generalized supervised
computer vision algorithm. However, in the field of microbiology, generation
and annotation of a diverse dataset including field-taken images are time
consuming, costly, and in some cases impossible. Image to image translation
frameworks allow us to diversify the dataset by transferring images from one
domain to another. However, most existing image translation techniques require
a paired dataset (original image and its corresponding image in the target
domain), which poses a significant challenge in collecting such datasets. In
addition, the application of these image translation frameworks in microbiology
is rarely discussed. In this study, we aim to develop an unpaired GAN-based
(Generative Adversarial Network) image to image translation model for
microbiological images, and study how it can improve generalization ability of
object detection models. In this paper, we present an unpaired and unsupervised
image translation model to translate laboratory-taken microbiological images to
field images, building upon the recent advances in GAN networks and Perceptual
loss function. We propose a novel design for a GAN model, BioGAN, by utilizing
Adversarial and Perceptual loss in order to transform high level features of
laboratory-taken images into field images, while keeping their spatial
features. The contribution of Adversarial and Perceptual loss in the generation
of realistic field images were studied. We used the synthetic field images,
generated by BioGAN, to train an object-detection framework, and compared the
results with those of an object-detection framework trained with laboratory
images; this resulted in up to 68.1% and 75.3% improvement on F1-score and mAP,
respectively. Codes is publicly available at
https://github.com/Kahroba2000/BioGAN.
|
[
"eess.IV",
"cs.CV"
] | false |
2306.05642
|
2023-06-09T03:02:36Z
|
Customizing General-Purpose Foundation Models for Medical Report
Generation
|
[
"Bang Yang",
"Asif Raza",
"Yuexian Zou",
"Tong Zhang"
] |
Medical caption prediction which can be regarded as a task of medical report
generation (MRG), requires the automatic generation of coherent and accurate
captions for the given medical images. However, the scarcity of labelled
medical image-report pairs presents great challenges in the development of deep
and large-scale neural networks capable of harnessing the potential artificial
general intelligence power like large language models (LLMs). In this work, we
propose customizing off-the-shelf general-purpose large-scale pre-trained
models, i.e., foundation models (FMs), in computer vision and natural language
processing with a specific focus on medical report generation. Specifically,
following BLIP-2, a state-of-the-art vision-language pre-training approach, we
introduce our encoder-decoder-based MRG model. This model utilizes a
lightweight query Transformer to connect two FMs: the giant vision Transformer
EVA-ViT-g and a bilingual LLM trained to align with human intentions (referred
to as ChatGLM-6B). Furthermore, we conduct ablative experiments on the
trainable components of the model to identify the crucial factors for effective
transfer learning. Our findings demonstrate that unfreezing EVA-ViT-g to learn
medical image representations, followed by parameter-efficient training of
ChatGLM-6B to capture the writing styles of medical reports, is essential for
achieving optimal results. Our best attempt (PCLmed Team) achieved the 4th and
the 2nd, respectively, out of 13 participating teams, based on the BERTScore
and ROUGE-1 metrics, in the ImageCLEFmedical Caption 2023 Caption Prediction
Task competition.
|
[
"cs.CV",
"cs.AI",
"cs.CL",
"cs.IR"
] | false |
2306.05670
|
2023-06-09T04:59:24Z
|
One-Shot Machine Unlearning with Mnemonic Code
|
[
"Tomoya Yamashita",
"Masanori Yamada",
"Takashi Shibata"
] |
Deep learning has achieved significant improvements in accuracy and has been
applied to various fields. With the spread of deep learning, a new problem has
also emerged; deep learning models can sometimes have undesirable information
from an ethical standpoint. This problem must be resolved if deep learning is
to make sensitive decisions such as hiring and prison sentencing. Machine
unlearning (MU) is the research area that responds to such demands. MU aims at
forgetting about undesirable training data from a trained deep learning model.
A naive MU approach is to re-train the whole model with the training data from
which the undesirable data has been removed. However, re-training the whole
model can take a huge amount of time and consumes significant computer
resources. To make MU even more practical, a simple-yet-effective MU method is
required. In this paper, we propose a one-shot MU method, which does not need
additional training. To design one-shot MU, we add noise to the model
parameters that are sensitive to undesirable information. In our proposed
method, we use the Fisher information matrix (FIM) to estimate the sensitive
model parameters. Training data were usually used to evaluate the FIM in
existing methods. In contrast, we avoid the need to retain the training data
for calculating the FIM by using class-specific synthetic signals called
mnemonic code. Extensive experiments using artificial and natural datasets
demonstrate that our method outperforms the existing methods.
|
[
"cs.LG",
"cs.AI",
"cs.CV"
] | false |
2306.05704
|
2023-06-09T06:50:20Z
|
Exploring Effective Mask Sampling Modeling for Neural Image Compression
|
[
"Lin Liu",
"Mingming Zhao",
"Shanxin Yuan",
"Wenlong Lyu",
"Wengang Zhou",
"Houqiang Li",
"Yanfeng Wang",
"Qi Tian"
] |
Image compression aims to reduce the information redundancy in images. Most
existing neural image compression methods rely on side information from
hyperprior or context models to eliminate spatial redundancy, but rarely
address the channel redundancy. Inspired by the mask sampling modeling in
recent self-supervised learning methods for natural language processing and
high-level vision, we propose a novel pretraining strategy for neural image
compression. Specifically, Cube Mask Sampling Module (CMSM) is proposed to
apply both spatial and channel mask sampling modeling to image compression in
the pre-training stage. Moreover, to further reduce channel redundancy, we
propose the Learnable Channel Mask Module (LCMM) and the Learnable Channel
Completion Module (LCCM). Our plug-and-play CMSM, LCMM, LCCM modules can apply
to both CNN-based and Transformer-based architectures, significantly reduce the
computational cost, and improve the quality of images. Experiments on the
public Kodak and Tecnick datasets demonstrate that our method achieves
competitive performance with lower computational complexity compared to
state-of-the-art image compression methods.
|
[
"cs.CV",
"cs.MM",
"eess.IV"
] | false |
2306.05844
|
2023-06-09T12:18:14Z
|
How Object Information Improves Skeleton-based Human Action Recognition
in Assembly Tasks
|
[
"Dustin Aganian",
"Mona Köhler",
"Sebastian Baake",
"Markus Eisenbach",
"Horst-Michael Gross"
] |
As the use of collaborative robots (cobots) in industrial manufacturing
continues to grow, human action recognition for effective human-robot
collaboration becomes increasingly important. This ability is crucial for
cobots to act autonomously and assist in assembly tasks. Recently,
skeleton-based approaches are often used as they tend to generalize better to
different people and environments. However, when processing skeletons alone,
information about the objects a human interacts with is lost. Therefore, we
present a novel approach of integrating object information into skeleton-based
action recognition. We enhance two state-of-the-art methods by treating object
centers as further skeleton joints. Our experiments on the assembly dataset
IKEA ASM show that our approach improves the performance of these
state-of-the-art methods to a large extent when combining skeleton joints with
objects predicted by a state-of-the-art instance segmentation model. Our
research sheds light on the benefits of combining skeleton joints with object
information for human action recognition in assembly tasks. We analyze the
effect of the object detector on the combination for action classification and
discuss the important factors that must be taken into account.
|
[
"cs.CV",
"cs.LG",
"cs.RO"
] | false |
2306.06094
|
2023-06-09T17:57:01Z
|
Leveraging Large Language Models for Scalable Vector Graphics-Driven
Image Understanding
|
[
"Mu Cai",
"Zeyi Huang",
"Yuheng Li",
"Haohan Wang",
"Yong Jae Lee"
] |
Recently, large language models (LLMs) have made significant advancements in
natural language understanding and generation. However, their potential in
computer vision remains largely unexplored. In this paper, we introduce a new,
exploratory approach that enables LLMs to process images using the Scalable
Vector Graphics (SVG) format. By leveraging the XML-based textual descriptions
of SVG representations instead of raster images, we aim to bridge the gap
between the visual and textual modalities, allowing LLMs to directly understand
and manipulate images without the need for parameterized visual components. Our
method facilitates simple image classification, generation, and in-context
learning using only LLM capabilities. We demonstrate the promise of our
approach across discriminative and generative tasks, highlighting its (i)
robustness against distribution shift, (ii) substantial improvements achieved
by tapping into the in-context learning abilities of LLMs, and (iii) image
understanding and generation capabilities with human guidance. Our code, data,
and models can be found here https://github.com/mu-cai/svg-llm.
|
[
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | false |
2306.06139
|
2023-06-09T07:00:00Z
|
WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern
Approaches for Mass Data Mining
|
[
"Ravindrakumar Purohit",
"Jai Prakash Verma",
"Rachna Jain",
"Madhuri Bhavsar"
] |
Weighted Outlier Detection is a method for identifying unusual or anomalous
data points in a dataset, which can be caused by various factors like human
error, fraud, or equipment malfunctions. Detecting outliers can reveal vital
information about system faults, fraudulent activities, and patterns in the
data, assisting experts in addressing the root causes of these anomalies.
However,creating a model of normal data patterns to identify outliers can be
challenging due to the nature of input data, labeled data availability, and
specific requirements of the problem. This article proposed the
WePaMaDM-Outlier Detection with distinct mass data mining domain, demonstrating
that such techniques are domain-dependent and usually developed for specific
problem formulations. Nevertheless, similar domains can adapt solutions with
modifications. This work also investigates the significance of data modeling in
outlier detection techniques in surveillance, fault detection, and trend
analysis, also referred to as novelty detection, a semisupervised task where
the algorithm learns to recognize abnormality while being taught the normal
class.
|
[
"cs.LG",
"cs.AI",
"cs.CV"
] | false |
2306.06233
|
2023-06-09T20:08:46Z
|
Boosting GUI Prototyping with Diffusion Models
|
[
"Jialiang Wei",
"Anne-Lise Courbis",
"Thomas Lambolais",
"Binbin Xu",
"Pierre Louis Bernard",
"Gérard Dray"
] |
GUI (graphical user interface) prototyping is a widely-used technique in
requirements engineering for gathering and refining requirements, reducing
development risks and increasing stakeholder engagement. However, GUI
prototyping can be a time-consuming and costly process. In recent years, deep
learning models such as Stable Diffusion have emerged as a powerful
text-to-image tool capable of generating detailed images based on text prompts.
In this paper, we propose UI-Diffuser, an approach that leverages Stable
Diffusion to generate mobile UIs through simple textual descriptions and UI
components. Preliminary results show that UI-Diffuser provides an efficient and
cost-effective way to generate mobile GUI designs while reducing the need for
extensive prototyping efforts. This approach has the potential to significantly
improve the speed and efficiency of GUI prototyping in requirements
engineering.
|
[
"cs.SE",
"cs.AI",
"cs.CV"
] | false |
2306.06254
|
2023-06-09T20:52:44Z
|
Understanding the Benefits of Image Augmentations
|
[
"Matthew Iceland",
"Christopher Kanan"
] |
Image Augmentations are widely used to reduce overfitting in neural networks.
However, the explainability of their benefits largely remains a mystery. We
study which layers of residual neural networks (ResNets) are most affected by
augmentations using Centered Kernel Alignment (CKA). We do so by analyzing
models of varying widths and depths, as well as whether their weights are
initialized randomly or through transfer learning. We find that the pattern of
how the layers are affected depends on the model's depth, and that networks
trained with augmentation that use information from two images affect the
learned weights significantly more than augmentations that operate on a single
image. Deeper layers of ResNets initialized with ImageNet-1K weights and
fine-tuned receive more impact from the augmentations than early layers.
Understanding the effects of image augmentations on CNNs will have a variety of
applications, such as determining how far back one needs to fine-tune a network
and which layers should be frozen when implementing layer freezing algorithms.
|
[
"cs.CV",
"cs.LG",
"eess.IV"
] | false |
2306.06269
|
2023-06-09T21:42:29Z
|
DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for
Urban Climate Resilience
|
[
"Wenlu Sun",
"Yao Sun",
"Chenying Liu",
"Conrad M Albrecht"
] |
Urban land use structures impact local climate conditions of metropolitan
areas. To shed light on the mechanism of local climate wrt. urban land use, we
present a novel, data-driven deep learning architecture and pipeline,
DeepLCZChange, to correlate airborne LiDAR data statistics with the Landsat 8
satellite's surface temperature product. A proof-of-concept numerical
experiment utilizes corresponding remote sensing data for the city of New York
to verify the cooling effect of urban forests.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2306.05682
|
2023-06-09T05:51:40Z
|
Lightweight Monocular Depth Estimation via Token-Sharing Transformer
|
[
"Dong-Jae Lee",
"Jae Young Lee",
"Hyounguk Shon",
"Eojindl Yi",
"Yeong-Hun Park",
"Sung-Sik Cho",
"Junmo Kim"
] |
Depth estimation is an important task in various robotics systems and
applications. In mobile robotics systems, monocular depth estimation is
desirable since a single RGB camera can be deployable at a low cost and compact
size. Due to its significant and growing needs, many lightweight monocular
depth estimation networks have been proposed for mobile robotics systems. While
most lightweight monocular depth estimation methods have been developed using
convolution neural networks, the Transformer has been gradually utilized in
monocular depth estimation recently. However, massive parameters and large
computational costs in the Transformer disturb the deployment to embedded
devices. In this paper, we present a Token-Sharing Transformer (TST), an
architecture using the Transformer for monocular depth estimation, optimized
especially in embedded devices. The proposed TST utilizes global token sharing,
which enables the model to obtain an accurate depth prediction with high
throughput in embedded devices. Experimental results show that TST outperforms
the existing lightweight monocular depth estimation methods. On the NYU Depth
v2 dataset, TST can deliver depth maps up to 63.4 FPS in NVIDIA Jetson nano and
142.6 FPS in NVIDIA Jetson TX2, with lower errors than the existing methods.
Furthermore, TST achieves real-time depth estimation of high-resolution images
on Jetson TX2 with competitive results.
|
[
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO",
"eess.IV"
] | false |
2306.05609
|
2023-06-09T00:54:21Z
|
Word sense extension
|
[
"Lei Yu",
"Yang Xu"
] |
Humans often make creative use of words to express novel senses. A
long-standing effort in natural language processing has been focusing on word
sense disambiguation (WSD), but little has been explored about how the sense
inventory of a word may be extended toward novel meanings. We present a
paradigm of word sense extension (WSE) that enables words to spawn new senses
toward novel context. We develop a framework that simulates novel word sense
extension by first partitioning a polysemous word type into two pseudo-tokens
that mark its different senses, and then inferring whether the meaning of a
pseudo-token can be extended to convey the sense denoted by the token
partitioned from the same word type. Our framework combines cognitive models of
chaining with a learning scheme that transforms a language model embedding
space to support various types of word sense extension. We evaluate our
framework against several competitive baselines and show that it is superior in
predicting plausible novel senses for over 7,500 English words. Furthermore, we
show that our WSE framework improves performance over a range of
transformer-based WSD models in predicting rare word senses with few or zero
mentions in the training data.
|
[
"cs.CL"
] | false |
2306.05672
|
2023-06-09T05:04:13Z
|
I run as fast as a rabbit, can you? A Multilingual Simile Dialogue
Dataset
|
[
"Longxuan Ma",
"Weinan Zhang",
"Shuhan Zhou",
"Churui Sun",
"Changxin Ke",
"Ting Liu"
] |
A simile is a figure of speech that compares two different things (called the
tenor and the vehicle) via shared properties. The tenor and the vehicle are
usually connected with comparator words such as "like" or "as". The simile
phenomena are unique and complex in a real-life dialogue scene where the tenor
and the vehicle can be verbal phrases or sentences, mentioned by different
speakers, exist in different sentences, or occur in reversed order. However,
the current simile research usually focuses on similes in a triplet tuple
(tenor, property, vehicle) or a single sentence where the tenor and vehicle are
usually entities or noun phrases, which could not reflect complex simile
phenomena in real scenarios. In this paper, we propose a novel and high-quality
multilingual simile dialogue (MSD) dataset to facilitate the study of complex
simile phenomena. The MSD is the largest manually annotated simile data
($\sim$20K) and it contains both English and Chinese data. Meanwhile, the MSD
data can also be used on dialogue tasks to test the ability of dialogue systems
when using similes. We design 3 simile tasks (recognition, interpretation, and
generation) and 2 dialogue tasks (retrieval and generation) with MSD. For each
task, we provide experimental results from strong pre-trained or
state-of-the-art models. The experiments demonstrate the challenge of MSD and
we have released the data/code on GitHub.
|
[
"cs.CL",
"I.2.7"
] | false |
2306.05827
|
2023-06-09T11:57:57Z
|
Towards the Exploitation of LLM-based Chatbot for Providing Legal
Support to Palestinian Cooperatives
|
[
"Rabee Qasem",
"Banan Tantour",
"Mohammed Maree"
] |
With the ever-increasing utilization of natural language processing (NLP), we
started to witness over the past few years a significant transformation in our
interaction with legal texts. This technology has advanced the analysis and
enhanced the understanding of complex legal terminology and contexts. The
development of recent large language models (LLMs), particularly ChatGPT, has
also introduced a revolutionary contribution to the way that legal texts can be
processed and comprehended. In this paper, we present our work on a
cooperative-legal question-answering LLM-based chatbot, where we developed a
set of legal questions about Palestinian cooperatives, associated with their
regulations and compared the auto-generated answers by the chatbot to their
correspondences that are designed by a legal expert. To evaluate the proposed
chatbot, we have used 50 queries generated by the legal expert and compared the
answers produced by the chart to their relevance judgments. Finding
demonstrated that an overall accuracy rate of 82% has been achieved when
answering the queries, while exhibiting an F1 score equivalent to 79%.
|
[
"cs.CL"
] | false |
2306.05871
|
2023-06-09T13:03:53Z
|
Towards a Robust Detection of Language Model Generated Text: Is ChatGPT
that Easy to Detect?
|
[
"Wissam Antoun",
"Virginie Mouilleron",
"Benoît Sagot",
"Djamé Seddah"
] |
Recent advances in natural language processing (NLP) have led to the
development of large language models (LLMs) such as ChatGPT. This paper
proposes a methodology for developing and evaluating ChatGPT detectors for
French text, with a focus on investigating their robustness on out-of-domain
data and against common attack schemes. The proposed method involves
translating an English dataset into French and training a classifier on the
translated data. Results show that the detectors can effectively detect
ChatGPT-generated text, with a degree of robustness against basic attack
techniques in in-domain settings. However, vulnerabilities are evident in
out-of-domain contexts, highlighting the challenge of detecting adversarial
text. The study emphasizes caution when applying in-domain testing results to a
wider variety of content. We provide our translated datasets and models as
open-source resources. https://gitlab.inria.fr/wantoun/robust-chatgpt-detection
|
[
"cs.CL"
] | false |
2306.05969
|
2023-06-09T15:35:11Z
|
Language Models Can Learn Exceptions to Syntactic Rules
|
[
"Cara Su-Yi Leong",
"Tal Linzen"
] |
Artificial neural networks can generalize productively to novel contexts. Can
they also learn exceptions to those productive rules? We explore this question
using the case of restrictions on English passivization (e.g., the fact that
"The vacation lasted five days" is grammatical, but "*Five days was lasted by
the vacation" is not). We collect human acceptability judgments for passive
sentences with a range of verbs, and show that the probability distribution
defined by GPT-2, a language model, matches the human judgments with high
correlation. We also show that the relative acceptability of a verb in the
active vs. passive voice is positively correlated with the relative frequency
of its occurrence in those voices. These results provide preliminary support
for the entrenchment hypothesis, according to which learners track and uses the
distributional properties of their input to learn negative exceptions to rules.
At the same time, this hypothesis fails to explain the magnitude of
unpassivizability demonstrated by certain individual verbs, suggesting that
other cues to exceptionality are available in the linguistic input.
|
[
"cs.CL"
] | false |
2306.06058
|
2023-06-09T17:32:45Z
|
Assisting Language Learners: Automated Trans-Lingual Definition
Generation via Contrastive Prompt Learning
|
[
"Hengyuan Zhang",
"Dawei Li",
"Yanran Li",
"Chenming Shang",
"Chufan Shi",
"Yong Jiang"
] |
The standard definition generation task requires to automatically produce
mono-lingual definitions (e.g., English definitions for English words), but
ignores that the generated definitions may also consist of unfamiliar words for
language learners. In this work, we propose a novel task of Trans-Lingual
Definition Generation (TLDG), which aims to generate definitions in another
language, i.e., the native speaker's language. Initially, we explore the
unsupervised manner of this task and build up a simple implementation of
fine-tuning the multi-lingual machine translation model. Then, we develop two
novel methods, Prompt Combination and Contrastive Prompt Learning, for further
enhancing the quality of the generation. Our methods are evaluated against the
baseline Pipeline method in both rich- and low-resource settings, and we
empirically establish its superiority in generating higher-quality
trans-lingual definitions.
|
[
"cs.CL"
] | false |
2306.06141
|
2023-06-09T07:10:01Z
|
Zero-Shot Dialogue Relation Extraction by Relating Explainable Triggers
and Relation Names
|
[
"Ze-Song Xu",
"Yun-Nung Chen"
] |
Developing dialogue relation extraction (DRE) systems often requires a large
amount of labeled data, which can be costly and time-consuming to annotate. In
order to improve scalability and support diverse, unseen relation extraction,
this paper proposes a method for leveraging the ability to capture triggers and
relate them to previously unseen relation names. Specifically, we introduce a
model that enables zero-shot dialogue relation extraction by utilizing
trigger-capturing capabilities. Our experiments on a benchmark DialogRE dataset
demonstrate that the proposed model achieves significant improvements for both
seen and unseen relations. Notably, this is the first attempt at zero-shot
dialogue relation extraction using trigger-capturing capabilities, and our
results suggest that this approach is effective for inferring previously unseen
relation types. Overall, our findings highlight the potential for this method
to enhance the scalability and practicality of DRE systems.
|
[
"cs.CL"
] | false |
2306.06205
|
2023-06-09T19:15:20Z
|
Morphosyntactic probing of multilingual BERT models
|
[
"Judit Acs",
"Endre Hamerlik",
"Roy Schwartz",
"Noah A. Smith",
"Andras Kornai"
] |
We introduce an extensive dataset for multilingual probing of morphological
information in language models (247 tasks across 42 languages from 10
families), each consisting of a sentence with a target word and a morphological
tag as the desired label, derived from the Universal Dependencies treebanks. We
find that pre-trained Transformer models (mBERT and XLM-RoBERTa) learn features
that attain strong performance across these tasks. We then apply two methods to
locate, for each probing task, where the disambiguating information resides in
the input. The first is a new perturbation method that masks various parts of
context; the second is the classical method of Shapley values. The most
intriguing finding that emerges is a strong tendency for the preceding context
to hold more information relevant to the prediction than the following context.
|
[
"cs.CL"
] | false |
2306.06221
|
2023-06-09T19:36:18Z
|
Conformalizing Machine Translation Evaluation
|
[
"Chrysoula Zerva",
"André F. T. Martins"
] |
Several uncertainty estimation methods have been recently proposed for
machine translation evaluation. While these methods can provide a useful
indication of when not to trust model predictions, we show in this paper that
the majority of them tend to underestimate model uncertainty, and as a result
they often produce misleading confidence intervals that do not cover the ground
truth. We propose as an alternative the use of conformal prediction, a
distribution-free method to obtain confidence intervals with a theoretically
established guarantee on coverage. First, we demonstrate that split conformal
prediction can ``correct'' the confidence intervals of previous methods to
yield a desired coverage level. Then, we highlight biases in estimated
confidence intervals, both in terms of the translation language pairs and the
quality of translations. We apply conditional conformal prediction techniques
to obtain calibration subsets for each data subgroup, leading to equalized
coverage.
|
[
"cs.CL"
] | false |
2306.06246
|
2023-06-09T20:42:11Z
|
Record Deduplication for Entity Distribution Modeling in ASR Transcripts
|
[
"Tianyu Huang",
"Chung Hoon Hong",
"Carl Wivagg",
"Kanna Shimizu"
] |
Voice digital assistants must keep up with trending search queries. We rely
on a speech recognition model using contextual biasing with a rapidly updated
set of entities, instead of frequent model retraining, to keep up with trends.
There are several challenges with this approach: (1) the entity set must be
frequently reconstructed, (2) the entity set is of limited size due to latency
and accuracy trade-offs, and (3) finding the true entity distribution for
biasing is complicated by ASR misrecognition. We address these challenges and
define an entity set by modeling customers true requested entity distribution
from ASR output in production using record deduplication, a technique from the
field of entity resolution. Record deduplication resolves or deduplicates
coreferences, including misrecognitions, of the same latent entity. Our method
successfully retrieves 95% of misrecognized entities and when used for
contextual biasing shows an estimated 5% relative word error rate reduction.
|
[
"cs.CL"
] | false |
2306.05605
|
2023-06-09T00:33:30Z
|
A Unified Generative Approach to Product Attribute-Value Identification
|
[
"Keiji Shinzato",
"Naoki Yoshinaga",
"Yandi Xia",
"Wei-Te Chen"
] |
Product attribute-value identification (PAVI) has been studied to link
products on e-commerce sites with their attribute values (e.g., <Material,
Cotton>) using product text as clues. Technical demands from real-world
e-commerce platforms require PAVI methods to handle unseen values,
multi-attribute values, and canonicalized values, which are only partly
addressed in existing extraction- and classification-based approaches.
Motivated by this, we explore a generative approach to the PAVI task. We
finetune a pre-trained generative model, T5, to decode a set of attribute-value
pairs as a target sequence from the given product text. Since the attribute
value pairs are unordered set elements, how to linearize them will matter; we,
thus, explore methods of composing an attribute-value pair and ordering the
pairs for the task. Experimental results confirm that our generation-based
approach outperforms the existing extraction and classification-based methods
on large-scale real-world datasets meant for those methods.
|
[
"cs.CL",
"cs.AI"
] | false |
2306.05779
|
2023-06-09T09:46:38Z
|
Transformer-based Time-to-Event Prediction for Chronic Kidney Disease
Deterioration
|
[
"Moshe Zisser",
"Dvir Aran"
] |
Deep-learning techniques, particularly the transformer model, have shown
great potential in enhancing the prediction performance of longitudinal health
records. While previous methods have mainly focused on fixed-time risk
prediction, time-to-event prediction (also known as survival analysis) is often
more appropriate for clinical scenarios. Here, we present a novel deep-learning
architecture we named STRAFE, a generalizable survival analysis
transformer-based architecture for electronic health records. The performance
of STRAFE was evaluated using a real-world claim dataset of over 130,000
individuals with stage 3 chronic kidney disease (CKD) and was found to
outperform other time-to-event prediction algorithms in predicting the exact
time of deterioration to stage 5. Additionally, STRAFE was found to outperform
binary outcome algorithms in predicting fixed-time risk, possibly due to its
ability to train on censored data. We show that STRAFE predictions can improve
the positive predictive value of high-risk patients by 3-fold, demonstrating
possible usage to improve targeting for intervention programs. Finally, we
suggest a novel visualization approach to predictions on a per-patient basis.
In conclusion, STRAFE is a cutting-edge time-to-event prediction algorithm that
has the potential to enhance risk predictions in large claims datasets.
|
[
"cs.LG",
"cs.CL"
] | false |
2306.06029
|
2023-06-09T16:50:02Z
|
HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence
for Digital Medicine
|
[
"Rodrigo Agerri",
"Iñigo Alonso",
"Aitziber Atutxa",
"Ander Berrondo",
"Ainara Estarrona",
"Iker Garcia-Ferrero",
"Iakes Goenaga",
"Koldo Gojenola",
"Maite Oronoz",
"Igor Perez-Tejedor",
"German Rigau",
"Anar Yeginbergenova"
] |
Providing high quality explanations for AI predictions based on machine
learning is a challenging and complex task. To work well it requires, among
other factors: selecting a proper level of generality/specificity of the
explanation; considering assumptions about the familiarity of the explanation
beneficiary with the AI task under consideration; referring to specific
elements that have contributed to the decision; making use of additional
knowledge (e.g. expert evidence) which might not be part of the prediction
process; and providing evidence supporting negative hypothesis. Finally, the
system needs to formulate the explanation in a clearly interpretable, and
possibly convincing, way. Given these considerations, ANTIDOTE fosters an
integrated vision of explainable AI, where low-level characteristics of the
deep learning process are combined with higher level schemes proper of the
human argumentation capacity. ANTIDOTE will exploit cross-disciplinary
competences in deep learning and argumentation to support a broader and
innovative view of explainable AI, where the need for high-quality explanations
for clinical cases deliberation is critical. As a first result of the project,
we publish the Antidote CasiMedicos dataset to facilitate research on
explainable AI in general, and argumentation in the medical domain in
particular.
|
[
"cs.CL",
"cs.AI"
] | false |
2306.06085
|
2023-06-09T17:48:54Z
|
Trapping LLM Hallucinations Using Tagged Context Prompts
|
[
"Philip Feldman",
"James R. Foulds",
"Shimei Pan"
] |
Recent advances in large language models (LLMs), such as ChatGPT, have led to
highly sophisticated conversation agents. However, these models suffer from
"hallucinations," where the model generates false or fabricated information.
Addressing this challenge is crucial, particularly with AI-driven platforms
being adopted across various sectors. In this paper, we propose a novel method
to recognize and flag instances when LLMs perform outside their domain
knowledge, and ensuring users receive accurate information.
We find that the use of context combined with embedded tags can successfully
combat hallucinations within generative language models. To do this, we
baseline hallucination frequency in no-context prompt-response pairs using
generated URLs as easily-tested indicators of fabricated data. We observed a
significant reduction in overall hallucination when context was supplied along
with question prompts for tested generative engines. Lastly, we evaluated how
placing tags within contexts impacted model responses and were able to
eliminate hallucinations in responses with 98.88% effectiveness.
|
[
"cs.CL",
"cs.AI",
"I.2.7; K.4.2"
] | false |
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