categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2403.08822
| null | null |
http://arxiv.org/pdf/2403.08822v1
|
2024-02-28T06:50:10Z
|
2024-02-28T06:50:10Z
|
LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient
Fine-Tuning of Large Language Models
|
In addressing the computational and memory demands of fine-tuning Large Language Models(LLMs), we propose LoRA-SP(Streamlined Partial Parameter Adaptation), a novel approach utilizing randomized half-selective parameter freezing within the Low-Rank Adaptation(LoRA)framework. This method efficiently balances pre-trained knowledge retention and adaptability for task-specific optimizations. Through a randomized mechanism, LoRA-SP determines which parameters to update or freeze, significantly reducing computational and memory requirements without compromising model performance. We evaluated LoRA-SP across several benchmark NLP tasks, demonstrating its ability to achieve competitive performance with substantially lower resource consumption compared to traditional full-parameter fine-tuning and other parameter-efficient techniques. LoRA-SP innovative approach not only facilitates the deployment of advanced NLP models in resource-limited settings but also opens new research avenues into effective and efficient model adaptation strategies.
|
[
"['Yichao Wu' 'Yafei Xiang' 'Shuning Huo' 'Yulu Gong' 'Penghao Liang']"
] |
null | null |
2403.08826
| null | null |
http://arxiv.org/pdf/2403.08826v1
|
2024-03-10T16:00:41Z
|
2024-03-10T16:00:41Z
|
A Dataset for the Validation of Truth Inference Algorithms Suitable for
Online Deployment
|
For the purpose of efficient and cost-effective large-scale data labeling, crowdsourcing is increasingly being utilized. To guarantee the quality of data labeling, multiple annotations need to be collected for each data sample, and truth inference algorithms have been developed to accurately infer the true labels. Despite previous studies having released public datasets to evaluate the efficacy of truth inference algorithms, these have typically focused on a single type of crowdsourcing task and neglected the temporal information associated with workers' annotation activities. These limitations significantly restrict the practical applicability of these algorithms, particularly in the context of long-term and online truth inference. In this paper, we introduce a substantial crowdsourcing annotation dataset collected from a real-world crowdsourcing platform. This dataset comprises approximately two thousand workers, one million tasks, and six million annotations. The data was gathered over a period of approximately six months from various types of tasks, and the timestamps of each annotation were preserved. We analyze the characteristics of the dataset from multiple perspectives and evaluate the effectiveness of several representative truth inference algorithms on this dataset. We anticipate that this dataset will stimulate future research on tracking workers' abilities over time in relation to different types of tasks, as well as enhancing online truth inference.
|
[
"['Fei Wang' 'Haoyu Liu' 'Haoyang Bi' 'Xiangzhuang Shen' 'Renyu Zhu'\n 'Runze Wu' 'Minmin Lin' 'Tangjie Lv' 'Changjie Fan' 'Qi Liu'\n 'Zhenya Huang' 'Enhong Chen']"
] |
null | null |
2403.08829
| null | null |
http://arxiv.org/pdf/2403.08829v1
|
2024-03-11T12:08:08Z
|
2024-03-11T12:08:08Z
|
Mitigating Biases in Collective Decision-Making: Enhancing Performance
in the Face of Fake News
|
Individual and social biases undermine the effectiveness of human advisers by inducing judgment errors which can disadvantage protected groups. In this paper, we study the influence these biases can have in the pervasive problem of fake news by evaluating human participants' capacity to identify false headlines. By focusing on headlines involving sensitive characteristics, we gather a comprehensive dataset to explore how human responses are shaped by their biases. Our analysis reveals recurring individual biases and their permeation into collective decisions. We show that demographic factors, headline categories, and the manner in which information is presented significantly influence errors in human judgment. We then use our collected data as a benchmark problem on which we evaluate the efficacy of adaptive aggregation algorithms. In addition to their improved accuracy, our results highlight the interactions between the emergence of collective intelligence and the mitigation of participant biases.
|
[
"['Axel Abels' 'Elias Fernandez Domingos' 'Ann Nowé' 'Tom Lenaerts']"
] |
null | null |
2403.08831
| null | null |
http://arxiv.org/pdf/2403.08831v1
|
2024-03-12T18:01:30Z
|
2024-03-12T18:01:30Z
|
Majority-of-Three: The Simplest Optimal Learner?
|
Developing an optimal PAC learning algorithm in the realizable setting, where empirical risk minimization (ERM) is suboptimal, was a major open problem in learning theory for decades. The problem was finally resolved by Hanneke a few years ago. Unfortunately, Hanneke's algorithm is quite complex as it returns the majority vote of many ERM classifiers that are trained on carefully selected subsets of the data. It is thus a natural goal to determine the simplest algorithm that is optimal. In this work we study the arguably simplest algorithm that could be optimal: returning the majority vote of three ERM classifiers. We show that this algorithm achieves the optimal in-expectation bound on its error which is provably unattainable by a single ERM classifier. Furthermore, we prove a near-optimal high-probability bound on this algorithm's error. We conjecture that a better analysis will prove that this algorithm is in fact optimal in the high-probability regime.
|
[
"['Ishaq Aden-Ali' 'Mikael Møller Høgsgaard' 'Kasper Green Larsen'\n 'Nikita Zhivotovskiy']"
] |
null | null |
2403.08834
| null | null |
http://arxiv.org/pdf/2403.08834v1
|
2024-03-13T08:04:00Z
|
2024-03-13T08:04:00Z
|
Predictive Analysis of Tuberculosis Treatment Outcomes Using Machine
Learning: A Karnataka TB Data Study at a Scale
|
Tuberculosis (TB) remains a global health threat, ranking among the leading causes of mortality worldwide. In this context, machine learning (ML) has emerged as a transformative force, providing innovative solutions to the complexities associated with TB treatment.This study explores how machine learning, especially with tabular data, can be used to predict Tuberculosis (TB) treatment outcomes more accurately. It transforms this prediction task into a binary classification problem, generating risk scores from patient data sourced from NIKSHAY, India's national TB control program, which includes over 500,000 patient records. Data preprocessing is a critical component of the study, and the model achieved an recall of 98% and an AUC-ROC score of 0.95 on the validation set, which includes 20,000 patient records.We also explore the use of Natural Language Processing (NLP) for improved model learning. Our results, corroborated by various metrics and ablation studies, validate the effectiveness of our approach. The study concludes by discussing the potential ramifications of our research on TB eradication efforts and proposing potential avenues for future work. This study marks a significant stride in the battle against TB, showcasing the potential of machine learning in healthcare.
|
[
"['SeshaSai Nath Chinagudaba' 'Darshan Gera' 'Krishna Kiran Vamsi Dasu'\n 'Uma Shankar S' 'Kiran K' 'Anil Singarajpure' 'Shivayogappa. U'\n 'Somashekar N' 'Vineet Kumar Chadda' 'Sharath B N']"
] |
null | null |
2403.08835
| null | null |
http://arxiv.org/pdf/2403.08835v1
|
2024-03-13T08:10:18Z
|
2024-03-13T08:10:18Z
|
Stacking-based deep neural network for player scouting in football 1
|
Datascouting is one of the most known data applications in professional sport, and specifically football. Its objective is to analyze huge database of players in order to detect high potentials that can be then individually considered by human scouts. In this paper, we propose a stacking-based deep learning model to detect high potential football players. Applied on open-source database, our model obtains significantly better results that classical statistical methods.
|
[
"['Simon Lacan']"
] |
null | null |
2403.08836
| null | null |
http://arxiv.org/pdf/2403.08836v1
|
2024-03-13T08:15:18Z
|
2024-03-13T08:15:18Z
|
Structural Positional Encoding for knowledge integration in
transformer-based medical process monitoring
|
Predictive process monitoring is a process mining task aimed at forecasting information about a running process trace, such as the most correct next activity to be executed. In medical domains, predictive process monitoring can provide valuable decision support in atypical and nontrivial situations. Decision support and quality assessment in medicine cannot ignore domain knowledge, in order to be grounded on all the available information (which is not limited to data) and to be really acceptable by end users. In this paper, we propose a predictive process monitoring approach relying on the use of a {em transformer}, a deep learning architecture based on the attention mechanism. A major contribution of our work lies in the incorporation of ontological domain-specific knowledge, carried out through a graph positional encoding technique. The paper presents and discusses the encouraging experimental result we are collecting in the domain of stroke management.
|
[
"['Christopher Irwin' 'Marco Dossena' 'Giorgio Leonardi' 'Stefania Montani']"
] |
null | null |
2403.08837
| null | null |
http://arxiv.org/pdf/2403.08837v1
|
2024-03-13T08:39:21Z
|
2024-03-13T08:39:21Z
|
Cyclic Data Parallelism for Efficient Parallelism of Deep Neural
Networks
|
Training large deep learning models requires parallelization techniques to scale. In existing methods such as Data Parallelism or ZeRO-DP, micro-batches of data are processed in parallel, which creates two drawbacks: the total memory required to store the model's activations peaks at the end of the forward pass, and gradients must be simultaneously averaged at the end of the backpropagation step. We propose Cyclic Data Parallelism, a novel paradigm shifting the execution of the micro-batches from simultaneous to sequential, with a uniform delay. At the cost of a slight gradient delay, the total memory taken by activations is constant, and the gradient communications are balanced during the training step. With Model Parallelism, our technique reduces the number of GPUs needed, by sharing GPUs across micro-batches. Within the ZeRO-DP framework, our technique allows communication of the model states with point-to-point operations rather than a collective broadcast operation. We illustrate the strength of our approach on the CIFAR-10 and ImageNet datasets.
|
[
"['Louis Fournier' 'Edouard Oyallon']"
] |
null | null |
2403.08838
| null | null |
http://arxiv.org/pdf/2403.08838v2
|
2024-03-15T06:22:16Z
|
2024-03-13T12:05:02Z
|
Predictive Clustering of Vessel Behavior Based on Hierarchical
Trajectory Representation
|
Vessel trajectory clustering, which aims to find similar trajectory patterns, has been widely leveraged in overwater applications. Most traditional methods use predefined rules and thresholds to identify discrete vessel behaviors. They aim for high-quality clustering and conduct clustering on entire sequences, whether the original trajectory or its sub-trajectories, failing to represent their evolution. To resolve this problem, we propose a Predictive Clustering of Hierarchical Vessel Behavior (PC-HiV). PC-HiV first uses hierarchical representations to transform every trajectory into a behavioral sequence. Then, it predicts evolution at each timestamp of the sequence based on the representations. By applying predictive clustering and latent encoding, PC-HiV improves clustering and predictions simultaneously. Experiments on real AIS datasets demonstrate PC-HiV's superiority over existing methods, showcasing its effectiveness in capturing behavioral evolution discrepancies between vessel types (tramp vs. liner) and within emission control areas. Results show that our method outperforms NN-Kmeans and Robust DAA by 3.9% and 6.4% of the purity score.
|
[
"['Rui Zhang' 'Hanyue Wu' 'Zhenzhong Yin' 'Zhu Xiao' 'Yong Xiong'\n 'Kezhong Liu']"
] |
null | null |
2403.08839
| null | null |
http://arxiv.org/pdf/2403.08839v1
|
2024-03-13T12:08:27Z
|
2024-03-13T12:08:27Z
|
Learning-Enhanced Neighborhood Selection for the Vehicle Routing Problem
with Time Windows
|
Large Neighborhood Search (LNS) is a universal approach that is broadly applicable and has proven to be highly efficient in practice for solving optimization problems. We propose to integrate machine learning (ML) into LNS to assist in deciding which parts of the solution should be destroyed and repaired in each iteration of LNS. We refer to our new approach as Learning-Enhanced Neighborhood Selection (LENS for short). Our approach is universally applicable, i.e., it can be applied to any LNS algorithm to amplify the workings of the destroy algorithm. In this paper, we demonstrate the potential of LENS on the fundamental Vehicle Routing Problem with Time Windows (VRPTW). We implemented an LNS algorithm for VRPTW and collected data on generated novel training instances derived from well-known, extensively utilized benchmark datasets. We trained our LENS approach with this data and compared the experimental results of our approach with two benchmark algorithms: a random neighborhood selection method to show that LENS learns to make informed choices and an oracle neighborhood selection method to demonstrate the potential of our LENS approach. With LENS, we obtain results that significantly improve the quality of the solutions.
|
[
"['Willem Feijen' 'Guido Schäfer' 'Koen Dekker' 'Seppo Pieterse']"
] |
null | null |
2403.08845
| null | null |
http://arxiv.org/pdf/2403.08845v2
|
2024-07-11T20:07:30Z
|
2024-03-13T16:30:57Z
|
Bifurcated Attention: Accelerating Massively Parallel Decoding with
Shared Prefixes in LLMs
|
This study introduces bifurcated attention, a method designed to enhance language model inference in shared-context batch decoding scenarios. Our approach addresses the challenge of redundant memory IO costs, a critical factor contributing to latency in high batch sizes and extended context lengths. Bifurcated attention achieves this by strategically dividing the attention mechanism during incremental decoding into two separate GEMM operations: one focusing on the KV cache from prefill, and another on the decoding process itself. While maintaining the computational load (FLOPs) of standard attention mechanisms, bifurcated attention ensures precise computation with significantly reduced memory IO. Our empirical results show over 2.1$times$ speedup when sampling 16 output sequences and more than 6.2$times$ speedup when sampling 32 sequences at context lengths exceeding 8k tokens on a 7B model that uses multi-head attention. The efficiency gains from bifurcated attention translate into lower latency, making it particularly suitable for real-time applications. For instance, it enables massively parallel answer generation without substantially increasing latency, thus enhancing performance when integrated with post-processing techniques such as re-ranking.
|
[
"['Ben Athiwaratkun' 'Sujan Kumar Gonugondla' 'Sanjay Krishna Gouda'\n 'Haifeng Qian' 'Hantian Ding' 'Qing Sun' 'Jun Wang' 'Jiacheng Guo'\n 'Liangfu Chen' 'Parminder Bhatia' 'Ramesh Nallapati' 'Sudipta Sengupta'\n 'Bing Xiang']"
] |
null | null |
2403.08847
| null | null |
http://arxiv.org/abs/2403.08847v2
|
2024-06-27T07:45:28Z
|
2024-03-13T16:50:04Z
|
JAXbind: Bind any function to JAX
|
JAX is widely used in machine learning and scientific computing, the latter of which often relies on existing high-performance code that we would ideally like to incorporate into JAX. Reimplementing the existing code in JAX is often impractical and the existing interface in JAX for binding custom code either limits the user to a single Jacobian product or requires deep knowledge of JAX and its C++ backend for general Jacobian products. With JAXbind we drastically reduce the effort required to bind custom functions implemented in other programming languages with full support for Jacobian-vector products and vector-Jacobian products to JAX. Specifically, JAXbind provides an easy-to-use Python interface for defining custom, so-called JAX primitives. Via JAXbind, any function callable from Python can be exposed as a JAX primitive. JAXbind allows a user to interface the JAX function transformation engine with custom derivatives and batching rules, enabling all JAX transformations for the custom primitive.
|
[
"['Jakob Roth' 'Martin Reinecke' 'Gordian Edenhofer']"
] |
null | null |
2403.08851
| null | null |
http://arxiv.org/pdf/2403.08851v1
|
2024-03-13T18:00:00Z
|
2024-03-13T18:00:00Z
|
PAPERCLIP: Associating Astronomical Observations and Natural Language
with Multi-Modal Models
|
We present PAPERCLIP (Proposal Abstracts Provide an Effective Representation for Contrastive Language-Image Pre-training), a method which associates astronomical observations imaged by telescopes with natural language using a neural network model. The model is fine-tuned from a pre-trained Contrastive Language-Image Pre-training (CLIP) model using successful observing proposal abstracts and corresponding downstream observations, with the abstracts optionally summarized via guided generation using large language models (LLMs). Using observations from the Hubble Space Telescope (HST) as an example, we show that the fine-tuned model embodies a meaningful joint representation between observations and natural language through tests targeting image retrieval (i.e., finding the most relevant observations using natural language queries) and description retrieval (i.e., querying for astrophysical object classes and use cases most relevant to a given observation). Our study demonstrates the potential for using generalist foundation models rather than task-specific models for interacting with astronomical data by leveraging text as an interface.
|
[
"['Siddharth Mishra-Sharma' 'Yiding Song' 'Jesse Thaler']"
] |
null | null |
2403.08854
| null | null |
http://arxiv.org/pdf/2403.08854v1
|
2024-03-13T18:00:01Z
|
2024-03-13T18:00:01Z
|
Moments of Clarity: Streamlining Latent Spaces in Machine Learning using
Moment Pooling
|
Many machine learning applications involve learning a latent representation of data, which is often high-dimensional and difficult to directly interpret. In this work, we propose "Moment Pooling", a natural extension of Deep Sets networks which drastically decrease latent space dimensionality of these networks while maintaining or even improving performance. Moment Pooling generalizes the summation in Deep Sets to arbitrary multivariate moments, which enables the model to achieve a much higher effective latent dimensionality for a fixed latent dimension. We demonstrate Moment Pooling on the collider physics task of quark/gluon jet classification by extending Energy Flow Networks (EFNs) to Moment EFNs. We find that Moment EFNs with latent dimensions as small as 1 perform similarly to ordinary EFNs with higher latent dimension. This small latent dimension allows for the internal representation to be directly visualized and interpreted, which in turn enables the learned internal jet representation to be extracted in closed form.
|
[
"['Rikab Gambhir' 'Athis Osathapan' 'Jesse Thaler']"
] |
null | null |
2403.08879
| null | null |
http://arxiv.org/abs/2403.08879v1
|
2024-03-13T18:05:16Z
|
2024-03-13T18:05:16Z
|
Multi-Objective Optimization Using Adaptive Distributed Reinforcement
Learning
|
The Intelligent Transportation System (ITS) environment is known to be dynamic and distributed, where participants (vehicle users, operators, etc.) have multiple, changing and possibly conflicting objectives. Although Reinforcement Learning (RL) algorithms are commonly applied to optimize ITS applications such as resource management and offloading, most RL algorithms focus on single objectives. In many situations, converting a multi-objective problem into a single-objective one is impossible, intractable or insufficient, making such RL algorithms inapplicable. We propose a multi-objective, multi-agent reinforcement learning (MARL) algorithm with high learning efficiency and low computational requirements, which automatically triggers adaptive few-shot learning in a dynamic, distributed and noisy environment with sparse and delayed reward. We test our algorithm in an ITS environment with edge cloud computing. Empirical results show that the algorithm is quick to adapt to new environments and performs better in all individual and system metrics compared to the state-of-the-art benchmark. Our algorithm also addresses various practical concerns with its modularized and asynchronous online training method. In addition to the cloud simulation, we test our algorithm on a single-board computer and show that it can make inference in 6 milliseconds.
|
[
"['Jing Tan' 'Ramin Khalili' 'Holger Karl']"
] |
null | null |
2403.08880
| null | null |
http://arxiv.org/abs/2403.08880v1
|
2024-03-13T18:06:43Z
|
2024-03-13T18:06:43Z
|
REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP
Values
|
Feature selection is a crucial step in building machine learning models. This process is often achieved with accuracy as an objective, and can be cumbersome and computationally expensive for large-scale datasets. Several additional model performance characteristics such as fairness and robustness are of importance for model development. As regulations are driving the need for more trustworthy models, deployed models need to be corrected for model characteristics associated with responsible artificial intelligence. When feature selection is done with respect to one model performance characteristic (eg. accuracy), feature selection with secondary model performance characteristics (eg. fairness and robustness) as objectives would require going through the computationally expensive selection process from scratch. In this paper, we introduce the problem of feature emph{reselection}, so that features can be selected with respect to secondary model performance characteristics efficiently even after a feature selection process has been done with respect to a primary objective. To address this problem, we propose REFRESH, a method to reselect features so that additional constraints that are desirable towards model performance can be achieved without having to train several new models. REFRESH's underlying algorithm is a novel technique using SHAP values and correlation analysis that can approximate for the predictions of a model without having to train these models. Empirical evaluations on three datasets, including a large-scale loan defaulting dataset show that REFRESH can help find alternate models with better model characteristics efficiently. We also discuss the need for reselection and REFRESH based on regulation desiderata.
|
[
"['Shubham Sharma' 'Sanghamitra Dutta' 'Emanuele Albini' 'Freddy Lecue'\n 'Daniele Magazzeni' 'Manuela Veloso']"
] |
null | null |
2403.08896
| null | null |
http://arxiv.org/pdf/2403.08896v2
|
2024-06-01T02:10:42Z
|
2024-03-13T18:37:16Z
|
One-Shot Averaging for Distributed TD($λ$) Under Markov Sampling
|
We consider a distributed setup for reinforcement learning, where each agent has a copy of the same Markov Decision Process but transitions are sampled from the corresponding Markov chain independently by each agent. We show that in this setting, we can achieve a linear speedup for TD($lambda$), a family of popular methods for policy evaluation, in the sense that $N$ agents can evaluate a policy $N$ times faster provided the target accuracy is small enough. Notably, this speedup is achieved by ``one shot averaging,'' a procedure where the agents run TD($lambda$) with Markov sampling independently and only average their results after the final step. This significantly reduces the amount of communication required to achieve a linear speedup relative to previous work.
|
[
"['Haoxing Tian' 'Ioannis Ch. Paschalidis' 'Alex Olshevsky']"
] |
null | null |
2403.08901
| null | null |
http://arxiv.org/pdf/2403.08901v3
|
2024-05-14T02:17:13Z
|
2024-03-13T18:45:51Z
|
A Framework for Strategic Discovery of Credible Neural Network Surrogate
Models under Uncertainty
|
The widespread integration of deep neural networks in developing data-driven surrogate models for high-fidelity simulations of complex physical systems highlights the critical necessity for robust uncertainty quantification techniques and credibility assessment methodologies, ensuring the reliable deployment of surrogate models in consequential decision-making. This study presents the Occam Plausibility Algorithm for surrogate models (OPAL-surrogate), providing a systematic framework to uncover predictive neural network-based surrogate models within the large space of potential models, including various neural network classes and choices of architecture and hyperparameters. The framework is grounded in hierarchical Bayesian inferences and employs model validation tests to evaluate the credibility and prediction reliability of the surrogate models under uncertainty. Leveraging these principles, OPAL-surrogate introduces a systematic and efficient strategy for balancing the trade-off between model complexity, accuracy, and prediction uncertainty. The effectiveness of OPAL-surrogate is demonstrated through two modeling problems, including the deformation of porous materials for building insulation and turbulent combustion flow for the ablation of solid fuels within hybrid rocket motors.
|
[
"['Pratyush Kumar Singh' 'Kathryn A. Farrell-Maupin' 'Danial Faghihi']"
] |
null | null |
2403.08917
| null | null |
http://arxiv.org/pdf/2403.08917v1
|
2024-03-13T19:19:19Z
|
2024-03-13T19:19:19Z
|
Efficiently Computing Similarities to Private Datasets
|
Many methods in differentially private model training rely on computing the similarity between a query point (such as public or synthetic data) and private data. We abstract out this common subroutine and study the following fundamental algorithmic problem: Given a similarity function $f$ and a large high-dimensional private dataset $X subset mathbb{R}^d$, output a differentially private (DP) data structure which approximates $sum_{x in X} f(x,y)$ for any query $y$. We consider the cases where $f$ is a kernel function, such as $f(x,y) = e^{-|x-y|_2^2/sigma^2}$ (also known as DP kernel density estimation), or a distance function such as $f(x,y) = |x-y|_2$, among others. Our theoretical results improve upon prior work and give better privacy-utility trade-offs as well as faster query times for a wide range of kernels and distance functions. The unifying approach behind our results is leveraging `low-dimensional structures' present in the specific functions $f$ that we study, using tools such as provable dimensionality reduction, approximation theory, and one-dimensional decomposition of the functions. Our algorithms empirically exhibit improved query times and accuracy over prior state of the art. We also present an application to DP classification. Our experiments demonstrate that the simple methodology of classifying based on average similarity is orders of magnitude faster than prior DP-SGD based approaches for comparable accuracy.
|
[
"['Arturs Backurs' 'Zinan Lin' 'Sepideh Mahabadi' 'Sandeep Silwal'\n 'Jakub Tarnawski']"
] |
null | null |
2403.08938
| null | null |
http://arxiv.org/pdf/2403.08938v1
|
2024-03-13T20:12:03Z
|
2024-03-13T20:12:03Z
|
A non-asymptotic theory of Kernel Ridge Regression: deterministic
equivalents, test error, and GCV estimator
|
We consider learning an unknown target function $f_*$ using kernel ridge regression (KRR) given i.i.d. data $(u_i,y_i)$, $ileq n$, where $u_i in U$ is a covariate vector and $y_i = f_* (u_i) +varepsilon_i in mathbb{R}$. A recent string of work has empirically shown that the test error of KRR can be well approximated by a closed-form estimate derived from an `equivalent' sequence model that only depends on the spectrum of the kernel operator. However, a theoretical justification for this equivalence has so far relied either on restrictive assumptions -- such as subgaussian independent eigenfunctions -- , or asymptotic derivations for specific kernels in high dimensions. In this paper, we prove that this equivalence holds for a general class of problems satisfying some spectral and concentration properties on the kernel eigendecomposition. Specifically, we establish in this setting a non-asymptotic deterministic approximation for the test error of KRR -- with explicit non-asymptotic bounds -- that only depends on the eigenvalues and the target function alignment to the eigenvectors of the kernel. Our proofs rely on a careful derivation of deterministic equivalents for random matrix functionals in the dimension free regime pioneered by Cheng and Montanari (2022). We apply this setting to several classical examples and show an excellent agreement between theoretical predictions and numerical simulations. These results rely on having access to the eigendecomposition of the kernel operator. Alternatively, we prove that, under this same setting, the generalized cross-validation (GCV) estimator concentrates on the test error uniformly over a range of ridge regularization parameter that includes zero (the interpolating solution). As a consequence, the GCV estimator can be used to estimate from data the test error and optimal regularization parameter for KRR.
|
[
"['Theodor Misiakiewicz' 'Basil Saeed']"
] |
null | null |
2403.08939
| null | null |
http://arxiv.org/pdf/2403.08939v1
|
2024-03-13T20:13:25Z
|
2024-03-13T20:13:25Z
|
FogGuard: guarding YOLO against fog using perceptual loss
|
In this paper, we present a novel fog-aware object detection network called FogGuard, designed to address the challenges posed by foggy weather conditions. Autonomous driving systems heavily rely on accurate object detection algorithms, but adverse weather conditions can significantly impact the reliability of deep neural networks (DNNs). Existing approaches fall into two main categories, 1) image enhancement such as IA-YOLO 2) domain adaptation based approaches. Image enhancement based techniques attempt to generate fog-free image. However, retrieving a fogless image from a foggy image is a much harder problem than detecting objects in a foggy image. Domain-adaptation based approaches, on the other hand, do not make use of labelled datasets in the target domain. Both categories of approaches are attempting to solve a harder version of the problem. Our approach builds over fine-tuning on the Our framework is specifically designed to compensate for foggy conditions present in the scene, ensuring robust performance even. We adopt YOLOv3 as the baseline object detection algorithm and introduce a novel Teacher-Student Perceptual loss, to high accuracy object detection in foggy images. Through extensive evaluations on common datasets such as PASCAL VOC and RTTS, we demonstrate the improvement in performance achieved by our network. We demonstrate that FogGuard achieves 69.43% mAP, as compared to 57.78% for YOLOv3 on the RTTS dataset. Furthermore, we show that while our training method increases time complexity, it does not introduce any additional overhead during inference compared to the regular YOLO network.
|
[
"['Soheil Gharatappeh' 'Sepideh Neshatfar' 'Salimeh Yasaei Sekeh'\n 'Vikas Dhiman']"
] |
null | null |
2403.08941
| null | null |
http://arxiv.org/pdf/2403.08941v2
|
2024-06-12T19:15:38Z
|
2024-03-13T20:16:21Z
|
Towards Model-Agnostic Posterior Approximation for Fast and Accurate
Variational Autoencoders
|
Inference for Variational Autoencoders (VAEs) consists of learning two models: (1) a generative model, which transforms a simple distribution over a latent space into the distribution over observed data, and (2) an inference model, which approximates the posterior of the latent codes given data. The two components are learned jointly via a lower bound to the generative model's log marginal likelihood. In early phases of joint training, the inference model poorly approximates the latent code posteriors. Recent work showed that this leads optimization to get stuck in local optima, negatively impacting the learned generative model. As such, recent work suggests ensuring a high-quality inference model via iterative training: maximizing the objective function relative to the inference model before every update to the generative model. Unfortunately, iterative training is inefficient, requiring heuristic criteria for reverting from iterative to joint training for speed. Here, we suggest an inference method that trains the generative and inference models independently. It approximates the posterior of the true model a priori; fixing this posterior approximation, we then maximize the lower bound relative to only the generative model. By conventional wisdom, this approach should rely on the true prior and likelihood of the true model to approximate its posterior (which are unknown). However, we show that we can compute a deterministic, model-agnostic posterior approximation (MAPA) of the true model's posterior. We then use MAPA to develop a proof-of-concept inference method. We present preliminary results on low-dimensional synthetic data that (1) MAPA captures the trend of the true posterior, and (2) our MAPA-based inference performs better density estimation with less computation than baselines. Lastly, we present a roadmap for scaling the MAPA-based inference method to high-dimensional data.
|
[
"['Yaniv Yacoby' 'Weiwei Pan' 'Finale Doshi-Velez']"
] |
null | null |
2403.08946
| null | null |
http://arxiv.org/pdf/2403.08946v1
|
2024-03-13T20:25:27Z
|
2024-03-13T20:25:27Z
|
Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM
Era
|
Explainable AI (XAI) refers to techniques that provide human-understandable insights into the workings of AI models. Recently, the focus of XAI is being extended towards Large Language Models (LLMs) which are often criticized for their lack of transparency. This extension calls for a significant transformation in XAI methodologies because of two reasons. First, many existing XAI methods cannot be directly applied to LLMs due to their complexity advanced capabilities. Second, as LLMs are increasingly deployed across diverse industry applications, the role of XAI shifts from merely opening the "black box" to actively enhancing the productivity and applicability of LLMs in real-world settings. Meanwhile, unlike traditional machine learning models that are passive recipients of XAI insights, the distinct abilities of LLMs can reciprocally enhance XAI. Therefore, in this paper, we introduce Usable XAI in the context of LLMs by analyzing (1) how XAI can benefit LLMs and AI systems, and (2) how LLMs can contribute to the advancement of XAI. We introduce 10 strategies, introducing the key techniques for each and discussing their associated challenges. We also provide case studies to demonstrate how to obtain and leverage explanations. The code used in this paper can be found at: https://github.com/JacksonWuxs/UsableXAI_LLM.
|
[
"['Xuansheng Wu' 'Haiyan Zhao' 'Yaochen Zhu' 'Yucheng Shi' 'Fan Yang'\n 'Tianming Liu' 'Xiaoming Zhai' 'Wenlin Yao' 'Jundong Li' 'Mengnan Du'\n 'Ninghao Liu']"
] |
null | null |
2403.08955
| null | null |
http://arxiv.org/pdf/2403.08955v1
|
2024-03-13T20:50:49Z
|
2024-03-13T20:50:49Z
|
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration
Complexity Analysis
|
Reinforcement Learning (RL) has shown exceptional performance across various applications, enabling autonomous agents to learn optimal policies through interaction with their environments. However, traditional RL frameworks often face challenges in terms of iteration complexity and robustness. Risk-sensitive RL, which balances expected return and risk, has been explored for its potential to yield probabilistically robust policies, yet its iteration complexity analysis remains underexplored. In this study, we conduct a thorough iteration complexity analysis for the risk-sensitive policy gradient method, focusing on the REINFORCE algorithm and employing the exponential utility function. We obtain an iteration complexity of $mathcal{O}(epsilon^{-2})$ to reach an $epsilon$-approximate first-order stationary point (FOSP). We investigate whether risk-sensitive algorithms can achieve better iteration complexity compared to their risk-neutral counterparts. Our theoretical analysis demonstrates that risk-sensitive REINFORCE can have a reduced number of iterations required for convergence. This leads to improved iteration complexity, as employing the exponential utility does not entail additional computation per iteration. We characterize the conditions under which risk-sensitive algorithms can achieve better iteration complexity. Our simulation results also validate that risk-averse cases can converge and stabilize more quickly after approximately half of the episodes compared to their risk-neutral counterparts.
|
[
"['Rui Liu' 'Erfaun Noorani' 'Pratap Tokekar' 'John S. Baras']"
] |
null | null |
2403.08965
| null | null |
http://arxiv.org/pdf/2403.08965v1
|
2024-03-13T21:11:58Z
|
2024-03-13T21:11:58Z
|
Deep Learning Based Dynamics Identification and Linearization of Orbital
Problems using Koopman Theory
|
The study of the Two-Body and Circular Restricted Three-Body Problems in the field of aerospace engineering and sciences is deeply important because they help describe the motion of both celestial and artificial satellites. With the growing demand for satellites and satellite formation flying, fast and efficient control of these systems is becoming ever more important. Global linearization of these systems allows engineers to employ methods of control in order to achieve these desired results. We propose a data-driven framework for simultaneous system identification and global linearization of both the Two-Body Problem and Circular Restricted Three-Body Problem via deep learning-based Koopman Theory, i.e., a framework that can identify the underlying dynamics and globally linearize it into a linear time-invariant (LTI) system. The linear Koopman operator is discovered through purely data-driven training of a Deep Neural Network with a custom architecture. This paper displays the ability of the Koopman operator to generalize to various other Two-Body systems without the need for retraining. We also demonstrate the capability of the same architecture to be utilized to accurately learn a Koopman operator that approximates the Circular Restricted Three-Body Problem.
|
[
"['George Nehma' 'Madhur Tiwari' 'Manasvi Lingam']"
] |
null | null |
2403.08969
| null | null |
http://arxiv.org/pdf/2403.08969v1
|
2024-03-13T21:30:01Z
|
2024-03-13T21:30:01Z
|
The Full-scale Assembly Simulation Testbed (FAST) Dataset
|
In recent years, numerous researchers have begun investigating how virtual reality (VR) tracking and interaction data can be used for a variety of machine learning purposes, including user identification, predicting cybersickness, and estimating learning gains. One constraint for this research area is the dearth of open datasets. In this paper, we present a new open dataset captured with our VR-based Full-scale Assembly Simulation Testbed (FAST). This dataset consists of data collected from 108 participants (50 females, 56 males, 2 non-binary) learning how to assemble two distinct full-scale structures in VR. In addition to explaining how the dataset was collected and describing the data included, we discuss how the dataset may be used by future researchers.
|
[
"['Alec G. Moore' 'Tiffany D. Do' 'Nayan N. Chawla'\n 'Antonia Jimenez Iriarte' 'Ryan P. McMahan']"
] |
null | null |
2403.08978
| null | null |
http://arxiv.org/pdf/2403.08978v1
|
2024-03-13T22:06:03Z
|
2024-03-13T22:06:03Z
|
AutoGuide: Automated Generation and Selection of State-Aware Guidelines
for Large Language Model Agents
|
The primary limitation of large language models (LLMs) is their restricted understanding of the world. This poses significant difficulties for LLM-based agents, particularly in domains where pre-trained LLMs lack sufficient knowledge. In this paper, we introduce a novel framework, called AutoGuide, that bridges the knowledge gap in pre-trained LLMs by leveraging implicit knowledge in offline experiences. Specifically, AutoGuide effectively extracts knowledge embedded in offline data by extracting a set of state-aware guidelines. Importantly, each state-aware guideline is expressed in concise natural language and follows a conditional structure, clearly describing the state where it is applicable. As such, the resulting guidelines enable a principled way to provide helpful knowledge pertinent to an agent's current decision-making process. We show that our approach outperforms competitive LLM-based baselines by a large margin in sequential decision-making benchmarks.
|
[
"['Yao Fu' 'Dong-Ki Kim' 'Jaekyeom Kim' 'Sungryull Sohn'\n 'Lajanugen Logeswaran' 'Kyunghoon Bae' 'Honglak Lee']"
] |
null | null |
2403.08980
| null | null |
http://arxiv.org/pdf/2403.08980v1
|
2024-03-13T22:10:42Z
|
2024-03-13T22:10:42Z
|
Architectural Implications of Neural Network Inference for High
Data-Rate, Low-Latency Scientific Applications
|
With more scientific fields relying on neural networks (NNs) to process data incoming at extreme throughputs and latencies, it is crucial to develop NNs with all their parameters stored on-chip. In many of these applications, there is not enough time to go off-chip and retrieve weights. Even more so, off-chip memory such as DRAM does not have the bandwidth required to process these NNs as fast as the data is being produced (e.g., every 25 ns). As such, these extreme latency and bandwidth requirements have architectural implications for the hardware intended to run these NNs: 1) all NN parameters must fit on-chip, and 2) codesigning custom/reconfigurable logic is often required to meet these latency and bandwidth constraints. In our work, we show that many scientific NN applications must run fully on chip, in the extreme case requiring a custom chip to meet such stringent constraints.
|
[
"['Olivia Weng' 'Alexander Redding' 'Nhan Tran' 'Javier Mauricio Duarte'\n 'Ryan Kastner']"
] |
null | null |
2403.09030
| null | null |
http://arxiv.org/pdf/2403.09030v1
|
2024-03-14T01:46:30Z
|
2024-03-14T01:46:30Z
|
An AI-Driven Approach to Wind Turbine Bearing Fault Diagnosis from
Acoustic Signals
|
This study aimed to develop a deep learning model for the classification of bearing faults in wind turbine generators from acoustic signals. A convolutional LSTM model was successfully constructed and trained by using audio data from five predefined fault types for both training and validation. To create the dataset, raw audio signal data was collected and processed in frames to capture time and frequency domain information. The model exhibited outstanding accuracy on training samples and demonstrated excellent generalization ability during validation, indicating its proficiency of generalization capability. On the test samples, the model achieved remarkable classification performance, with an overall accuracy exceeding 99.5%, and a false positive rate of less than 1% for normal status. The findings of this study provide essential support for the diagnosis and maintenance of bearing faults in wind turbine generators, with the potential to enhance the reliability and efficiency of wind power generation.
|
[
"['Zhao Wang' 'Xiaomeng Li' 'Na Li' 'Longlong Shu']"
] |
null | null |
2403.09032
| null | null |
http://arxiv.org/pdf/2403.09032v1
|
2024-03-14T01:51:35Z
|
2024-03-14T01:51:35Z
|
CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language
Models to Coding Preferences
|
Evaluating the alignment of large language models (LLMs) with user-defined coding preferences is a challenging endeavour that requires assessing intricate textual LLMs' outputs. By relying on automated metrics and static analysis tools, existing benchmarks fail to assess nuances in user instructions and LLM outputs, highlighting the need for large-scale datasets and benchmarks for LLM preference alignment. In this paper, we introduce CodeUltraFeedback, a preference dataset of 10,000 complex instructions to tune and align LLMs to coding preferences through AI feedback. We generate responses to the instructions using a pool of 14 diverse LLMs, which we then annotate according to their alignment with five coding preferences using the LLM-as-a-Judge approach with GPT-3.5, producing both numerical and textual feedback. We also present CODAL-Bench, a benchmark for assessing LLM alignment with these coding preferences. Our results show that CodeLlama-7B-Instruct, aligned through reinforcement learning from AI feedback (RLAIF) with direct preference optimization (DPO) using CodeUltraFeedback's AI feedback data, outperforms 34B LLMs on CODAL-Bench, validating the utility of CodeUltraFeedback for preference tuning. Furthermore, we show our DPO-aligned CodeLlama model improves functional correctness on HumanEval+ compared to the unaligned base model. Therefore, our contributions bridge the gap in preference tuning of LLMs for code and set the stage for further advancements in model alignment and RLAIF for code intelligence. Our code and data are available at https://github.com/martin-wey/CodeUltraFeedback.
|
[
"['Martin Weyssow' 'Aton Kamanda' 'Houari Sahraoui']"
] |
null | null |
2403.09035
| null | null |
http://arxiv.org/pdf/2403.09035v1
|
2024-03-14T02:11:38Z
|
2024-03-14T02:11:38Z
|
DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers
|
Enabling efficient and accurate deep neural network (DNN) inference on microcontrollers is non-trivial due to the constrained on-chip resources. Current methodologies primarily focus on compressing larger models yet at the expense of model accuracy. In this paper, we rethink the problem from the inverse perspective by constructing small/weak models directly and improving their accuracy. Thus, we introduce DiTMoS, a novel DNN training and inference framework with a selector-classifiers architecture, where the selector routes each input sample to the appropriate classifier for classification. DiTMoS is grounded on a key insight: a composition of weak models can exhibit high diversity and the union of them can significantly boost the accuracy upper bound. To approach the upper bound, DiTMoS introduces three strategies including diverse training data splitting to increase the classifiers' diversity, adversarial selector-classifiers training to ensure synergistic interactions thereby maximizing their complementarity, and heterogeneous feature aggregation to improve the capacity of classifiers. We further propose a network slicing technique to alleviate the extra memory overhead incurred by feature aggregation. We deploy DiTMoS on the Neucleo STM32F767ZI board and evaluate it based on three time-series datasets for human activity recognition, keywords spotting, and emotion recognition, respectively. The experiment results manifest that: (a) DiTMoS achieves up to 13.4% accuracy improvement compared to the best baseline; (b) network slicing almost completely eliminates the memory overhead incurred by feature aggregation with a marginal increase of latency.
|
[
"['Xiao Ma' 'Shengfeng He' 'Hezhe Qiao' 'Dong Ma']"
] |
null | null |
2403.09039
| null | null |
http://arxiv.org/pdf/2403.09039v1
|
2024-03-14T02:26:10Z
|
2024-03-14T02:26:10Z
|
Spatial-temporal Memories Enhanced Graph Autoencoder for Anomaly
Detection in Dynamic Graphs
|
Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes. The conventional approaches that tackle this problem typically employ an unsupervised learning framework, capturing normality patterns with exclusive normal data during training and identifying deviations as anomalies during testing. However, these methods face critical drawbacks: they either only depend on proxy tasks for general representation without directly pinpointing normal patterns, or they neglect to differentiate between spatial and temporal normality patterns, leading to diminished efficacy in anomaly detection. To address these challenges, we introduce a novel Spatial-Temporal memories-enhanced graph autoencoder (STRIPE). Initially, STRIPE employs Graph Neural Networks (GNNs) and gated temporal convolution layers to extract spatial features and temporal features, respectively. Then STRIPE incorporates separate spatial and temporal memory networks, which capture and store prototypes of normal patterns, thereby preserving the uniqueness of spatial and temporal normality. After that, through a mutual attention mechanism, these stored patterns are then retrieved and integrated with encoded graph embeddings. Finally, the integrated features are fed into the decoder to reconstruct the graph streams which serve as the proxy task for anomaly detection. This comprehensive approach not only minimizes reconstruction errors but also refines the model by emphasizing the compactness and distinctiveness of the embeddings in relation to the nearest memory prototypes. Through extensive testing, STRIPE has demonstrated a superior capability to discern anomalies by effectively leveraging the distinct spatial and temporal dynamics of dynamic graphs, significantly outperforming existing methodologies, with an average improvement of 15.39% on AUC values.
|
[
"['Jie Liu' 'Xuequn Shang' 'Xiaolin Han' 'Wentao Zhang' 'Hongzhi Yin']"
] |
null | null |
2403.09048
| null | null |
http://arxiv.org/pdf/2403.09048v1
|
2024-03-14T02:36:16Z
|
2024-03-14T02:36:16Z
|
Taming Cross-Domain Representation Variance in Federated Prototype
Learning with Heterogeneous Data Domains
|
Federated learning (FL) allows collaborative machine learning training without sharing private data. While most FL methods assume identical data domains across clients, real-world scenarios often involve heterogeneous data domains. Federated Prototype Learning (FedPL) addresses this issue, using mean feature vectors as prototypes to enhance model generalization. However, existing FedPL methods create the same number of prototypes for each client, leading to cross-domain performance gaps and disparities for clients with varied data distributions. To mitigate cross-domain feature representation variance, we introduce FedPLVM, which establishes variance-aware dual-level prototypes clustering and employs a novel $alpha$-sparsity prototype loss. The dual-level prototypes clustering strategy creates local clustered prototypes based on private data features, then performs global prototypes clustering to reduce communication complexity and preserve local data privacy. The $alpha$-sparsity prototype loss aligns samples from underrepresented domains, enhancing intra-class similarity and reducing inter-class similarity. Evaluations on Digit-5, Office-10, and DomainNet datasets demonstrate our method's superiority over existing approaches.
|
[
"['Lei Wang' 'Jieming Bian' 'Letian Zhang' 'Chen Chen' 'Jie Xu']"
] |
null | null |
2403.09053
| null | null |
http://arxiv.org/pdf/2403.09053v2
|
2024-05-04T19:52:03Z
|
2024-03-14T02:42:19Z
|
Towards a theory of model distillation
|
Distillation is the task of replacing a complicated machine learning model with a simpler model that approximates the original [BCNM06,HVD15]. Despite many practical applications, basic questions about the extent to which models can be distilled, and the runtime and amount of data needed to distill, remain largely open. To study these questions, we initiate a general theory of distillation, defining PAC-distillation in an analogous way to PAC-learning [Val84]. As applications of this theory: (1) we propose new algorithms to extract the knowledge stored in the trained weights of neural networks -- we show how to efficiently distill neural networks into succinct, explicit decision tree representations when possible by using the ``linear representation hypothesis''; and (2) we prove that distillation can be much cheaper than learning from scratch, and make progress on characterizing its complexity.
|
[
"['Enric Boix-Adsera']"
] |
null | null |
2403.09054
| null | null |
http://arxiv.org/pdf/2403.09054v2
|
2024-04-06T00:22:37Z
|
2024-03-14T02:42:42Z
|
Keyformer: KV Cache Reduction through Key Tokens Selection for Efficient
Generative Inference
|
Transformers have emerged as the underpinning architecture for Large Language Models (LLMs). In generative language models, the inference process involves two primary phases: prompt processing and token generation. Token generation, which constitutes the majority of the computational workload, primarily entails vector-matrix multiplications and interactions with the Key-Value (KV) Cache. This phase is constrained by memory bandwidth due to the overhead of transferring weights and KV cache values from the memory system to the computing units. This memory bottleneck becomes particularly pronounced in applications that require long-context and extensive text generation, both of which are increasingly crucial for LLMs. This paper introduces "Keyformer", an innovative inference-time approach, to mitigate the challenges associated with KV cache size and memory bandwidth utilization. Keyformer leverages the observation that approximately 90% of the attention weight in generative inference focuses on a specific subset of tokens, referred to as "key" tokens. Keyformer retains only the key tokens in the KV cache by identifying these crucial tokens using a novel score function. This approach effectively reduces both the KV cache size and memory bandwidth usage without compromising model accuracy. We evaluate Keyformer's performance across three foundational models: GPT-J, Cerebras-GPT, and MPT, which employ various positional embedding algorithms. Our assessment encompasses a variety of tasks, with a particular emphasis on summarization and conversation tasks involving extended contexts. Keyformer's reduction of KV cache reduces inference latency by 2.1x and improves token generation throughput by 2.4x, while preserving the model's accuracy.
|
[
"['Muhammad Adnan' 'Akhil Arunkumar' 'Gaurav Jain' 'Prashant J. Nair'\n 'Ilya Soloveychik' 'Purushotham Kamath']"
] |
null | null |
2403.09066
| null | null |
http://arxiv.org/pdf/2403.09066v1
|
2024-03-14T03:13:01Z
|
2024-03-14T03:13:01Z
|
Hyperparameters in Continual Learning: a Reality Check
|
Various algorithms for continual learning (CL) have been designed with the goal of effectively alleviating the trade-off between stability and plasticity during the CL process. To achieve this goal, tuning appropriate hyperparameters for each algorithm is essential. As an evaluation protocol, it has been common practice to train a CL algorithm using diverse hyperparameter values on a CL scenario constructed with a benchmark dataset. Subsequently, the best performance attained with the optimal hyperparameter value serves as the criterion for evaluating the CL algorithm. In this paper, we contend that this evaluation protocol is not only impractical but also incapable of effectively assessing the CL capability of a CL algorithm. Returning to the fundamental principles of model evaluation in machine learning, we propose an evaluation protocol that involves Hyperparameter Tuning and Evaluation phases. Those phases consist of different datasets but share the same CL scenario. In the Hyperparameter Tuning phase, each algorithm is iteratively trained with different hyperparameter values to find the optimal hyperparameter values. Subsequently, in the Evaluation phase, the optimal hyperparameter values is directly applied for training each algorithm, and their performance in the Evaluation phase serves as the criterion for evaluating them. Through experiments on CIFAR-100 and ImageNet-100 based on the proposed protocol in class-incremental learning, we not only observed that the existing evaluation method fail to properly assess the CL capability of each algorithm but also observe that some recently proposed state-of-the-art algorithms, which reported superior performance, actually exhibit inferior performance compared to the previous algorithm.
|
[
"['Sungmin Cha' 'Kyunghyun Cho']"
] |
null | null |
2403.09086
| null | null |
http://arxiv.org/pdf/2403.09086v1
|
2024-03-14T04:06:45Z
|
2024-03-14T04:06:45Z
|
Learning from straggler clients in federated learning
|
How well do existing federated learning algorithms learn from client devices that return model updates with a significant time delay? Is it even possible to learn effectively from clients that report back minutes, hours, or days after being scheduled? We answer these questions by developing Monte Carlo simulations of client latency that are guided by real-world applications. We study synchronous optimization algorithms like FedAvg and FedAdam as well as the asynchronous FedBuff algorithm, and observe that all these existing approaches struggle to learn from severely delayed clients. To improve upon this situation, we experiment with modifications, including distillation regularization and exponential moving averages of model weights. Finally, we introduce two new algorithms, FARe-DUST and FeAST-on-MSG, based on distillation and averaging, respectively. Experiments with the EMNIST, CIFAR-100, and StackOverflow benchmark federated learning tasks demonstrate that our new algorithms outperform existing ones in terms of accuracy for straggler clients, while also providing better trade-offs between training time and total accuracy.
|
[
"['Andrew Hard' 'Antonious M. Girgis' 'Ehsan Amid' 'Sean Augenstein'\n 'Lara McConnaughey' 'Rajiv Mathews' 'Rohan Anil']"
] |
null | null |
2403.09090
| null | null |
http://arxiv.org/pdf/2403.09090v1
|
2024-03-14T04:26:00Z
|
2024-03-14T04:26:00Z
|
Dissipative Gradient Descent Ascent Method: A Control Theory Inspired
Algorithm for Min-max Optimization
|
Gradient Descent Ascent (GDA) methods for min-max optimization problems typically produce oscillatory behavior that can lead to instability, e.g., in bilinear settings. To address this problem, we introduce a dissipation term into the GDA updates to dampen these oscillations. The proposed Dissipative GDA (DGDA) method can be seen as performing standard GDA on a state-augmented and regularized saddle function that does not strictly introduce additional convexity/concavity. We theoretically show the linear convergence of DGDA in the bilinear and strongly convex-strongly concave settings and assess its performance by comparing DGDA with other methods such as GDA, Extra-Gradient (EG), and Optimistic GDA. Our findings demonstrate that DGDA surpasses these methods, achieving superior convergence rates. We support our claims with two numerical examples that showcase DGDA's effectiveness in solving saddle point problems.
|
[
"['Tianqi Zheng' 'Nicolas Loizou' 'Pengcheng You' 'Enrique Mallada']"
] |
null | null |
2403.09100
| null | null |
http://arxiv.org/pdf/2403.09100v1
|
2024-03-14T04:48:06Z
|
2024-03-14T04:48:06Z
|
Virtual birefringence imaging and histological staining of amyloid
deposits in label-free tissue using autofluorescence microscopy and deep
learning
|
Systemic amyloidosis is a group of diseases characterized by the deposition of misfolded proteins in various organs and tissues, leading to progressive organ dysfunction and failure. Congo red stain is the gold standard chemical stain for the visualization of amyloid deposits in tissue sections, as it forms complexes with the misfolded proteins and shows a birefringence pattern under polarized light microscopy. However, Congo red staining is tedious and costly to perform, and prone to false diagnoses due to variations in the amount of amyloid, staining quality and expert interpretation through manual examination of tissue under a polarization microscope. Here, we report the first demonstration of virtual birefringence imaging and virtual Congo red staining of label-free human tissue to show that a single trained neural network can rapidly transform autofluorescence images of label-free tissue sections into brightfield and polarized light microscopy equivalent images, matching the histochemically stained versions of the same samples. We demonstrate the efficacy of our method with blind testing and pathologist evaluations on cardiac tissue where the virtually stained images agreed well with the histochemically stained ground truth images. Our virtually stained polarization and brightfield images highlight amyloid birefringence patterns in a consistent, reproducible manner while mitigating diagnostic challenges due to variations in the quality of chemical staining and manual imaging processes as part of the clinical workflow.
|
[
"['Xilin Yang' 'Bijie Bai' 'Yijie Zhang' 'Musa Aydin' 'Sahan Yoruc Selcuk'\n 'Zhen Guo' 'Gregory A. Fishbein' 'Karine Atlan' 'William Dean Wallace'\n 'Nir Pillar' 'Aydogan Ozcan']"
] |
null | null |
2403.09101
| null | null |
http://arxiv.org/pdf/2403.09101v1
|
2024-03-14T04:48:31Z
|
2024-03-14T04:48:31Z
|
Soften to Defend: Towards Adversarial Robustness via Self-Guided Label
Refinement
|
Adversarial training (AT) is currently one of the most effective ways to obtain the robustness of deep neural networks against adversarial attacks. However, most AT methods suffer from robust overfitting, i.e., a significant generalization gap in adversarial robustness between the training and testing curves. In this paper, we first identify a connection between robust overfitting and the excessive memorization of noisy labels in AT from a view of gradient norm. As such label noise is mainly caused by a distribution mismatch and improper label assignments, we are motivated to propose a label refinement approach for AT. Specifically, our Self-Guided Label Refinement first self-refines a more accurate and informative label distribution from over-confident hard labels, and then it calibrates the training by dynamically incorporating knowledge from self-distilled models into the current model and thus requiring no external teachers. Empirical results demonstrate that our method can simultaneously boost the standard accuracy and robust performance across multiple benchmark datasets, attack types, and architectures. In addition, we also provide a set of analyses from the perspectives of information theory to dive into our method and suggest the importance of soft labels for robust generalization.
|
[
"['Daiwei Yu' 'Zhuorong Li' 'Lina Wei' 'Canghong Jin' 'Yun Zhang'\n 'Sixian Chan']"
] |
null | null |
2403.09107
| null | null |
http://arxiv.org/pdf/2403.09107v2
|
2024-04-11T07:42:43Z
|
2024-03-14T05:00:29Z
|
S^2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering
|
Anchor-based large-scale multi-view clustering has attracted considerable attention for its effectiveness in handling massive datasets. However, current methods mainly seek the consensus embedding feature for clustering by exploring global correlations between anchor graphs or projection matrices.In this paper, we propose a simple yet efficient scalable multi-view tensor clustering (S^2MVTC) approach, where our focus is on learning correlations of embedding features within and across views. Specifically, we first construct the embedding feature tensor by stacking the embedding features of different views into a tensor and rotating it. Additionally, we build a novel tensor low-frequency approximation (TLFA) operator, which incorporates graph similarity into embedding feature learning, efficiently achieving smooth representation of embedding features within different views. Furthermore, consensus constraints are applied to embedding features to ensure inter-view semantic consistency. Experimental results on six large-scale multi-view datasets demonstrate that S^2MVTC significantly outperforms state-of-the-art algorithms in terms of clustering performance and CPU execution time, especially when handling massive data. The code of S^2MVTC is publicly available at https://github.com/longzhen520/S2MVTC.
|
[
"['Zhen Long' 'Qiyuan Wang' 'Yazhou Ren' 'Yipeng Liu' 'Ce Zhu']"
] |
null | null |
2403.09110
| null | null |
http://arxiv.org/pdf/2403.09110v1
|
2024-03-14T05:17:39Z
|
2024-03-14T05:17:39Z
|
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
|
Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in environments with complicated dynamics, such as stabilizing the magnetohydrodynamics of a tokamak fusion reactor or minimizing the drag force exerted on an object in a fluid flow. However, these algorithms require an abundance of training examples and may become prohibitively expensive for many applications. In addition, the reliance on deep neural networks often results in an uninterpretable, black-box policy that may be too computationally expensive to use with certain embedded systems. Recent advances in sparse dictionary learning, such as the sparse identification of nonlinear dynamics (SINDy), have shown promise for creating efficient and interpretable data-driven models in the low-data regime. In this work we introduce SINDy-RL, a unifying framework for combining SINDy and DRL to create efficient, interpretable, and trustworthy representations of the dynamics model, reward function, and control policy. We demonstrate the effectiveness of our approaches on benchmark control environments and challenging fluids problems. SINDy-RL achieves comparable performance to state-of-the-art DRL algorithms using significantly fewer interactions in the environment and results in an interpretable control policy orders of magnitude smaller than a deep neural network policy.
|
[
"['Nicholas Zolman' 'Urban Fasel' 'J. Nathan Kutz' 'Steven L. Brunton']"
] |
null | null |
2403.09113
| null | null |
http://arxiv.org/pdf/2403.09113v2
|
2024-03-17T17:55:47Z
|
2024-03-14T05:29:35Z
|
AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based
on Meta Learning
|
Large-scale pretraining followed by task-specific finetuning has achieved great success in various NLP tasks. Since finetuning all parameters of large pretrained models poses substantial computational and memory challenges, several efficient finetuning methods have been developed. Among them, low-rank adaptation (LoRA), which finetunes low-rank incremental update matrices on top of frozen pretrained weights, has proven particularly effective. Nonetheless, LoRA's uniform rank assignment across all layers, along with its reliance on an exhaustive search to find the best rank, leads to high computation costs and suboptimal finetuning performance. To address these limitations, we introduce AutoLoRA, a meta learning based framework for automatically identifying the optimal rank of each LoRA layer. AutoLoRA associates each rank-1 matrix in a low-rank update matrix with a selection variable, which determines whether the rank-1 matrix should be discarded. A meta learning based method is developed to learn these selection variables. The optimal rank is determined by thresholding the values of these variables. Our comprehensive experiments on natural language understanding, generation, and sequence labeling demonstrate the effectiveness of AutoLoRA.
|
[
"['Ruiyi Zhang' 'Rushi Qiang' 'Sai Ashish Somayajula' 'Pengtao Xie']"
] |
null | null |
2403.09117
| null | null |
http://arxiv.org/abs/2403.09117v2
|
2024-06-05T07:49:25Z
|
2024-03-14T05:40:23Z
|
Randomized Principal Component Analysis for Hyperspectral Image
Classification
|
The high-dimensional feature space of the hyperspectral imagery poses major challenges to the processing and analysis of the hyperspectral data sets. In such a case, dimensionality reduction is necessary to decrease the computational complexity. The random projections open up new ways of dimensionality reduction, especially for large data sets. In this paper, the principal component analysis (PCA) and randomized principal component analysis (R-PCA) for the classification of hyperspectral images using support vector machines (SVM) and light gradient boosting machines (LightGBM) have been investigated. In this experimental research, the number of features was reduced to 20 and 30 for classification of two hyperspectral datasets (Indian Pines and Pavia University). The experimental results demonstrated that PCA outperformed R-PCA for SVM for both datasets, but received close accuracy values for LightGBM. The highest classification accuracies were obtained as 0.9925 and 0.9639 by LightGBM with original features for the Pavia University and Indian Pines, respectively.
|
[
"['Mustafa Ustuner']"
] |
null | null |
2403.09123
| null | null |
http://arxiv.org/pdf/2403.09123v1
|
2024-03-14T06:14:07Z
|
2024-03-14T06:14:07Z
|
Optimal Top-Two Method for Best Arm Identification and Fluid Analysis
|
Top-$2$ methods have become popular in solving the best arm identification (BAI) problem. The best arm, or the arm with the largest mean amongst finitely many, is identified through an algorithm that at any sequential step independently pulls the empirical best arm, with a fixed probability $beta$, and pulls the best challenger arm otherwise. The probability of incorrect selection is guaranteed to lie below a specified $delta >0$. Information theoretic lower bounds on sample complexity are well known for BAI problem and are matched asymptotically as $delta rightarrow 0$ by computationally demanding plug-in methods. The above top 2 algorithm for any $beta in (0,1)$ has sample complexity within a constant of the lower bound. However, determining the optimal $beta$ that matches the lower bound has proven difficult. In this paper, we address this and propose an optimal top-2 type algorithm. We consider a function of allocations anchored at a threshold. If it exceeds the threshold then the algorithm samples the empirical best arm. Otherwise, it samples the challenger arm. We show that the proposed algorithm is optimal as $delta rightarrow 0$. Our analysis relies on identifying a limiting fluid dynamics of allocations that satisfy a series of ordinary differential equations pasted together and that describe the asymptotic path followed by our algorithm. We rely on the implicit function theorem to show existence and uniqueness of these fluid ode's and to show that the proposed algorithm remains close to the ode solution.
|
[
"['Agniv Bandyopadhyay' 'Sandeep Juneja' 'Shubhada Agrawal']"
] |
null | null |
2403.09141
| null | null |
http://arxiv.org/pdf/2403.09141v1
|
2024-03-14T07:40:32Z
|
2024-03-14T07:40:32Z
|
Uncertainty Estimation in Multi-Agent Distributed Learning for
AI-Enabled Edge Devices
|
Initially considered as low-power units with limited autonomous processing, Edge IoT devices have seen a paradigm shift with the introduction of FPGAs and AI accelerators. This advancement has vastly amplified their computational capabilities, emphasizing the practicality of edge AI. Such progress introduces new challenges of optimizing AI tasks for the limitations of energy and network resources typical in Edge computing environments. Our study explores methods that enable distributed data processing through AI-enabled edge devices, enhancing collaborative learning capabilities. A key focus of our research is the challenge of determining confidence levels in learning outcomes, considering the spatial and temporal variability of data sets encountered by independent agents. To address this issue, we investigate the application of Bayesian neural networks, proposing a novel approach to manage uncertainty in distributed learning environments.
|
[
"['Gleb Radchenko' 'Victoria Andrea Fill']"
] |
null | null |
2403.09171
| null | null |
http://arxiv.org/pdf/2403.09171v1
|
2024-03-14T08:31:39Z
|
2024-03-14T08:31:39Z
|
ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks
|
Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by noisy and redundant graph data. As a prominent solution, Graph Augmentation Learning (GAL) has recently received increasing attention. Among prior GAL approaches, edge-dropping methods that randomly remove edges from a graph during training are effective techniques to improve the robustness of GNNs. However, randomly dropping edges often results in bypassing critical edges, consequently weakening the effectiveness of message passing. In this paper, we propose a novel adversarial edge-dropping method (ADEdgeDrop) that leverages an adversarial edge predictor guiding the removal of edges, which can be flexibly incorporated into diverse GNN backbones. Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method. The proposed ADEdgeDrop is optimized alternately by stochastic gradient descent and projected gradient descent. Comprehensive experiments on six graph benchmark datasets demonstrate that the proposed ADEdgeDrop outperforms state-of-the-art baselines across various GNN backbones, demonstrating improved generalization and robustness.
|
[
"['Zhaoliang Chen' 'Zhihao Wu' 'Ylli Sadikaj' 'Claudia Plant'\n 'Hong-Ning Dai' 'Shiping Wang' 'Wenzhong Guo']"
] |
null | null |
2403.09183
| null | null |
http://arxiv.org/pdf/2403.09183v1
|
2024-03-14T08:53:01Z
|
2024-03-14T08:53:01Z
|
Generalized Relevance Learning Grassmann Quantization
|
Due to advancements in digital cameras, it is easy to gather multiple images (or videos) from an object under different conditions. Therefore, image-set classification has attracted more attention, and different solutions were proposed to model them. A popular way to model image sets is subspaces, which form a manifold called the Grassmann manifold. In this contribution, we extend the application of Generalized Relevance Learning Vector Quantization to deal with Grassmann manifold. The proposed model returns a set of prototype subspaces and a relevance vector. While prototypes model typical behaviours within classes, the relevance factors specify the most discriminative principal vectors (or images) for the classification task. They both provide insights into the model's decisions by highlighting influential images and pixels for predictions. Moreover, due to learning prototypes, the model complexity of the new method during inference is independent of dataset size, unlike previous works. We applied it to several recognition tasks including handwritten digit recognition, face recognition, activity recognition, and object recognition. Experiments demonstrate that it outperforms previous works with lower complexity and can successfully model the variation, such as handwritten style or lighting conditions. Moreover, the presence of relevances makes the model robust to the selection of subspaces' dimensionality.
|
[
"['M. Mohammadi' 'M. Babai' 'M. H. F. Wilkinson']"
] |
null | null |
2403.09188
| null | null |
http://arxiv.org/pdf/2403.09188v1
|
2024-03-14T09:03:51Z
|
2024-03-14T09:03:51Z
|
Design of an basis-projected layer for sparse datasets in deep learning
training using gc-ms spectra as a case study
|
Deep learning (DL) models encompass millions or even billions of parameters and learn complex patterns from big data. However, not all data are initially stored in a suitable formation to effectively train a DL model, e.g., gas chromatography-mass spectrometry (GC-MS) spectra and DNA sequence. These datasets commonly contain many zero values, and the sparse data formation causes difficulties in optimizing DL models. A DL module called the basis-projected layer (BPL) was proposed to mitigate the issue by transforming the sparse data into a dense representation. The transformed data is expected to facilitate the gradient calculation and finetuned process in a DL training process. The dataset, example of a sparse dataset, contained 362 specialty coffee odorant spectra detected from GC-MS. The BPL layer was placed at the beginning of the DL model. The tunable parameters in the layer were learnable projected axes that were the bases of a new representation space. The layer rotated these bases when its parameters were updated. When the number of the bases was the same as the original dimension, the increasing percentage of the F1 scores was 8.56%. Furthermore, when the number was set as 768 (the original dimension was 490), the increasing percentage of the F1 score was 11.49%. The layer not only maintained the model performance and even constructed a better representation space in analyzing sparse datasets.
|
[
"['Yu Tang Chang' 'Shih Fang Chen']"
] |
null | null |
2403.09193
| null | null |
http://arxiv.org/pdf/2403.09193v1
|
2024-03-14T09:07:14Z
|
2024-03-14T09:07:14Z
|
Are Vision Language Models Texture or Shape Biased and Can We Steer
Them?
|
Vision language models (VLMs) have drastically changed the computer vision model landscape in only a few years, opening an exciting array of new applications from zero-shot image classification, over to image captioning, and visual question answering. Unlike pure vision models, they offer an intuitive way to access visual content through language prompting. The wide applicability of such models encourages us to ask whether they also align with human vision - specifically, how far they adopt human-induced visual biases through multimodal fusion, or whether they simply inherit biases from pure vision models. One important visual bias is the texture vs. shape bias, or the dominance of local over global information. In this paper, we study this bias in a wide range of popular VLMs. Interestingly, we find that VLMs are often more shape-biased than their vision encoders, indicating that visual biases are modulated to some extent through text in multimodal models. If text does indeed influence visual biases, this suggests that we may be able to steer visual biases not just through visual input but also through language: a hypothesis that we confirm through extensive experiments. For instance, we are able to steer shape bias from as low as 49% to as high as 72% through prompting alone. For now, the strong human bias towards shape (96%) remains out of reach for all tested VLMs.
|
[
"['Paul Gavrikov' 'Jovita Lukasik' 'Steffen Jung' 'Robert Geirhos'\n 'Bianca Lamm' 'Muhammad Jehanzeb Mirza' 'Margret Keuper' 'Janis Keuper']"
] |
null | null |
2403.09206
| null | null |
http://arxiv.org/pdf/2403.09206v1
|
2024-03-14T09:19:50Z
|
2024-03-14T09:19:50Z
|
Upper Bound of Bayesian Generalization Error in Partial Concept
Bottleneck Model (CBM): Partial CBM outperforms naive CBM
|
Concept Bottleneck Model (CBM) is a methods for explaining neural networks. In CBM, concepts which correspond to reasons of outputs are inserted in the last intermediate layer as observed values. It is expected that we can interpret the relationship between the output and concept similar to linear regression. However, this interpretation requires observing all concepts and decreases the generalization performance of neural networks. Partial CBM (PCBM), which uses partially observed concepts, has been devised to resolve these difficulties. Although some numerical experiments suggest that the generalization performance of PCBMs is almost as high as that of the original neural networks, the theoretical behavior of its generalization error has not been yet clarified since PCBM is singular statistical model. In this paper, we reveal the Bayesian generalization error in PCBM with a three-layered and linear architecture. The result indcates that the structure of partially observed concepts decreases the Bayesian generalization error compared with that of CBM (full-observed concepts).
|
[
"['Naoki Hayashi' 'Yoshihide Sawada']"
] |
null | null |
2403.09209
| null | null |
http://arxiv.org/pdf/2403.09209v2
|
2024-03-17T14:05:51Z
|
2024-03-14T09:22:17Z
|
LAN: Learning Adaptive Neighbors for Real-Time Insider Threat Detection
|
Enterprises and organizations are faced with potential threats from insider employees that may lead to serious consequences. Previous studies on insider threat detection (ITD) mainly focus on detecting abnormal users or abnormal time periods (e.g., a week or a day). However, a user may have hundreds of thousands of activities in the log, and even within a day there may exist thousands of activities for a user, requiring a high investigation budget to verify abnormal users or activities given the detection results. On the other hand, existing works are mainly post-hoc methods rather than real-time detection, which can not report insider threats in time before they cause loss. In this paper, we conduct the first study towards real-time ITD at activity level, and present a fine-grained and efficient framework LAN. Specifically, LAN simultaneously learns the temporal dependencies within an activity sequence and the relationships between activities across sequences with graph structure learning. Moreover, to mitigate the data imbalance problem in ITD, we propose a novel hybrid prediction loss, which integrates self-supervision signals from normal activities and supervision signals from abnormal activities into a unified loss for anomaly detection. We evaluate the performance of LAN on two widely used datasets, i.e., CERT r4.2 and CERT r5.2. Extensive and comparative experiments demonstrate the superiority of LAN, outperforming 9 state-of-the-art baselines by at least 9.92% and 6.35% in AUC for real-time ITD on CERT r4.2 and r5.2, respectively. Moreover, LAN can be also applied to post-hoc ITD, surpassing 8 competitive baselines by at least 7.70% and 4.03% in AUC on two datasets. Finally, the ablation study, parameter analysis, and compatibility analysis evaluate the impact of each module and hyper-parameter in LAN. The source code can be obtained from https://github.com/Li1Neo/LAN.
|
[
"['Xiangrui Cai' 'Yang Wang' 'Sihan Xu' 'Hao Li' 'Ying Zhang' 'Zheli Liu'\n 'Xiaojie Yuan']"
] |
null | null |
2403.09215
| null | null |
http://arxiv.org/pdf/2403.09215v1
|
2024-03-14T09:28:28Z
|
2024-03-14T09:28:28Z
|
On the Laplace Approximation as Model Selection Criterion for Gaussian
Processes
|
Model selection aims to find the best model in terms of accuracy, interpretability or simplicity, preferably all at once. In this work, we focus on evaluating model performance of Gaussian process models, i.e. finding a metric that provides the best trade-off between all those criteria. While previous work considers metrics like the likelihood, AIC or dynamic nested sampling, they either lack performance or have significant runtime issues, which severely limits applicability. We address these challenges by introducing multiple metrics based on the Laplace approximation, where we overcome a severe inconsistency occuring during naive application of the Laplace approximation. Experiments show that our metrics are comparable in quality to the gold standard dynamic nested sampling without compromising for computational speed. Our model selection criteria allow significantly faster and high quality model selection of Gaussian process models.
|
[
"['Andreas Besginow' 'Jan David Hüwel' 'Thomas Pawellek' 'Christian Beecks'\n 'Markus Lange-Hegermann']"
] |
null | null |
2403.09223
| null | null |
http://arxiv.org/pdf/2403.09223v1
|
2024-03-14T09:43:07Z
|
2024-03-14T09:43:07Z
|
MCformer: Multivariate Time Series Forecasting with Mixed-Channels
Transformer
|
The massive generation of time-series data by largescale Internet of Things (IoT) devices necessitates the exploration of more effective models for multivariate time-series forecasting. In previous models, there was a predominant use of the Channel Dependence (CD) strategy (where each channel represents a univariate sequence). Current state-of-the-art (SOTA) models primarily rely on the Channel Independence (CI) strategy. The CI strategy treats all channels as a single channel, expanding the dataset to improve generalization performance and avoiding inter-channel correlation that disrupts long-term features. However, the CI strategy faces the challenge of interchannel correlation forgetting. To address this issue, we propose an innovative Mixed Channels strategy, combining the data expansion advantages of the CI strategy with the ability to counteract inter-channel correlation forgetting. Based on this strategy, we introduce MCformer, a multivariate time-series forecasting model with mixed channel features. The model blends a specific number of channels, leveraging an attention mechanism to effectively capture inter-channel correlation information when modeling long-term features. Experimental results demonstrate that the Mixed Channels strategy outperforms pure CI strategy in multivariate time-series forecasting tasks.
|
[
"['Wenyong Han' 'Tao Zhu Member' 'Liming Chen' 'Huansheng Ning' 'Yang Luo'\n 'Yaping Wan']"
] |
null | null |
2403.09228
| null | null |
http://arxiv.org/pdf/2403.09228v1
|
2024-03-14T09:48:48Z
|
2024-03-14T09:48:48Z
|
Uncertainty Quantification for cross-subject Motor Imagery
classification
|
Uncertainty Quantification aims to determine when the prediction from a Machine Learning model is likely to be wrong. Computer Vision research has explored methods for determining epistemic uncertainty (also known as model uncertainty), which should correspond with generalisation error. These methods theoretically allow to predict misclassifications due to inter-subject variability. We applied a variety of Uncertainty Quantification methods to predict misclassifications for a Motor Imagery Brain Computer Interface. Deep Ensembles performed best, both in terms of classification performance and cross-subject Uncertainty Quantification performance. However, we found that standard CNNs with Softmax output performed better than some of the more advanced methods.
|
[
"['Prithviraj Manivannan' 'Ivo Pascal de Jong' 'Matias Valdenegro-Toro'\n 'Andreea Ioana Sburlea']"
] |
null | null |
2403.09259
| null | null |
http://arxiv.org/pdf/2403.09259v1
|
2024-03-14T10:33:28Z
|
2024-03-14T10:33:28Z
|
To Label or Not to Label: Hybrid Active Learning for Neural Machine
Translation
|
Active learning (AL) techniques reduce labeling costs for training neural machine translation (NMT) models by selecting smaller representative subsets from unlabeled data for annotation. Diversity sampling techniques select heterogeneous instances, while uncertainty sampling methods select instances with the highest model uncertainty. Both approaches have limitations - diversity methods may extract varied but trivial examples, while uncertainty sampling can yield repetitive, uninformative instances. To bridge this gap, we propose HUDS, a hybrid AL strategy for domain adaptation in NMT that combines uncertainty and diversity for sentence selection. HUDS computes uncertainty scores for unlabeled sentences and subsequently stratifies them. It then clusters sentence embeddings within each stratum using k-MEANS and computes diversity scores by distance to the centroid. A weighted hybrid score that combines uncertainty and diversity is then used to select the top instances for annotation in each AL iteration. Experiments on multi-domain German-English datasets demonstrate the better performance of HUDS over other strong AL baselines. We analyze the sentence selection with HUDS and show that it prioritizes diverse instances having high model uncertainty for annotation in early AL iterations.
|
[
"['Abdul Hameed Azeemi' 'Ihsan Ayyub Qazi' 'Agha Ali Raza']"
] |
null | null |
2403.09267
| null | null |
http://arxiv.org/pdf/2403.09267v4
|
2024-06-04T07:05:33Z
|
2024-03-14T10:44:10Z
|
Deep Limit Order Book Forecasting
|
We exploit cutting-edge deep learning methodologies to explore the predictability of high-frequency Limit Order Book mid-price changes for a heterogeneous set of stocks traded on the NASDAQ exchange. In so doing, we release `LOBFrame', an open-source code base to efficiently process large-scale Limit Order Book data and quantitatively assess state-of-the-art deep learning models' forecasting capabilities. Our results are twofold. We demonstrate that the stocks' microstructural characteristics influence the efficacy of deep learning methods and that their high forecasting power does not necessarily correspond to actionable trading signals. We argue that traditional machine learning metrics fail to adequately assess the quality of forecasts in the Limit Order Book context. As an alternative, we propose an innovative operational framework that evaluates predictions' practicality by focusing on the probability of accurately forecasting complete transactions. This work offers academics and practitioners an avenue to make informed and robust decisions on the application of deep learning techniques, their scope and limitations, effectively exploiting emergent statistical properties of the Limit Order Book.
|
[
"['Antonio Briola' 'Silvia Bartolucci' 'Tomaso Aste']"
] |
null | null |
2403.09284
| null | null |
http://arxiv.org/pdf/2403.09284v1
|
2024-03-14T11:12:10Z
|
2024-03-14T11:12:10Z
|
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning
|
Personalized federated learning becomes a hot research topic that can learn a personalized learning model for each client. Existing personalized federated learning models prefer to aggregate similar clients with similar data distribution to improve the performance of learning models. However, similaritybased personalized federated learning methods may exacerbate the class imbalanced problem. In this paper, we propose a novel Dynamic Affinity-based Personalized Federated Learning model (DA-PFL) to alleviate the class imbalanced problem during federated learning. Specifically, we build an affinity metric from a complementary perspective to guide which clients should be aggregated. Then we design a dynamic aggregation strategy to dynamically aggregate clients based on the affinity metric in each round to reduce the class imbalanced risk. Extensive experiments show that the proposed DA-PFL model can significantly improve the accuracy of each client in three real-world datasets with state-of-the-art comparison methods.
|
[
"['Xu Yang' 'Jiyuan Feng' 'Songyue Guo' 'Ye Wang' 'Ye Ding' 'Binxing Fang'\n 'Qing Liao']"
] |
null | null |
2403.09290
| null | null |
http://arxiv.org/pdf/2403.09290v1
|
2024-03-14T11:23:39Z
|
2024-03-14T11:23:39Z
|
SELECTOR: Heterogeneous graph network with convolutional masked
autoencoder for multimodal robust prediction of cancer survival
|
Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and significantly enhancing patients' quality of life. Multimodal prediction of cancer patient survival offers a more comprehensive and precise approach. However, existing methods still grapple with challenges related to missing multimodal data and information interaction within modalities. This paper introduces SELECTOR, a heterogeneous graph-aware network based on convolutional mask encoders for robust multimodal prediction of cancer patient survival. SELECTOR comprises feature edge reconstruction, convolutional mask encoder, feature cross-fusion, and multimodal survival prediction modules. Initially, we construct a multimodal heterogeneous graph and employ the meta-path method for feature edge reconstruction, ensuring comprehensive incorporation of feature information from graph edges and effective embedding of nodes. To mitigate the impact of missing features within the modality on prediction accuracy, we devised a convolutional masked autoencoder (CMAE) to process the heterogeneous graph post-feature reconstruction. Subsequently, the feature cross-fusion module facilitates communication between modalities, ensuring that output features encompass all features of the modality and relevant information from other modalities. Extensive experiments and analysis on six cancer datasets from TCGA demonstrate that our method significantly outperforms state-of-the-art methods in both modality-missing and intra-modality information-confirmed cases. Our codes are made available at https://github.com/panliangrui/Selector.
|
[
"['Liangrui Pan' 'Yijun Peng' 'Yan Li' 'Xiang Wang' 'Wenjuan Liu'\n 'Liwen Xu' 'Qingchun Liang' 'Shaoliang Peng']"
] |
null | null |
2403.09298
| null | null |
http://arxiv.org/pdf/2403.09298v1
|
2024-03-14T11:37:02Z
|
2024-03-14T11:37:02Z
|
More than words: Advancements and challenges in speech recognition for
singing
|
This paper addresses the challenges and advancements in speech recognition for singing, a domain distinctly different from standard speech recognition. Singing encompasses unique challenges, including extensive pitch variations, diverse vocal styles, and background music interference. We explore key areas such as phoneme recognition, language identification in songs, keyword spotting, and full lyrics transcription. I will describe some of my own experiences when performing research on these tasks just as they were starting to gain traction, but will also show how recent developments in deep learning and large-scale datasets have propelled progress in this field. My goal is to illuminate the complexities of applying speech recognition to singing, evaluate current capabilities, and outline future research directions.
|
[
"['Anna Kruspe']"
] |
null | null |
2403.09300
| null | null |
http://arxiv.org/pdf/2403.09300v1
|
2024-03-14T11:46:25Z
|
2024-03-14T11:46:25Z
|
Recursive Causal Discovery
|
Causal discovery, i.e., learning the causal graph from data, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scientific domains. Causal discovery is hampered by two main challenges: limited data results in errors in statistical testing and the computational complexity of the learning task is daunting. This paper builds upon and extends four of our prior publications (Mokhtarian et al., 2021; Akbari et al., 2021; Mokhtarian et al., 2022, 2023a). These works introduced the concept of removable variables, which are the only variables that can be removed recursively for the purpose of causal discovery. Presence and identification of removable variables allow recursive approaches for causal discovery, a promising solution that helps to address the aforementioned challenges by reducing the problem size successively. This reduction not only minimizes conditioning sets in each conditional independence (CI) test, leading to fewer errors but also significantly decreases the number of required CI tests. The worst-case performances of these methods nearly match the lower bound. In this paper, we present a unified framework for the proposed algorithms, refined with additional details and enhancements for a coherent presentation. A comprehensive literature review is also included, comparing the computational complexity of our methods with existing approaches, showcasing their state-of-the-art efficiency. Another contribution of this paper is the release of RCD, a Python package that efficiently implements these algorithms. This package is designed for practitioners and researchers interested in applying these methods in practical scenarios. The package is available at github.com/ban-epfl/rcd, with comprehensive documentation provided at rcdpackage.com.
|
[
"['Ehsan Mokhtarian' 'Sepehr Elahi' 'Sina Akbari' 'Negar Kiyavash']"
] |
null | null |
2403.09302
| null | null |
http://arxiv.org/pdf/2403.09302v2
|
2024-07-12T16:27:06Z
|
2024-03-14T11:49:43Z
|
StainFuser: Controlling Diffusion for Faster Neural Style Transfer in
Multi-Gigapixel Histology Images
|
Stain normalization algorithms aim to transform the color and intensity characteristics of a source multi-gigapixel histology image to match those of a target image, mitigating inconsistencies in the appearance of stains used to highlight cellular components in the images. We propose a new approach, StainFuser, which treats this problem as a style transfer task using a novel Conditional Latent Diffusion architecture, eliminating the need for handcrafted color components. With this method, we curate SPI-2M the largest stain normalization dataset to date of over 2 million histology images with neural style transfer for high-quality transformations. Trained on this data, StainFuser outperforms current state-of-the-art deep learning and handcrafted methods in terms of the quality of normalized images and in terms of downstream model performance on the CoNIC dataset.
|
[
"['Robert Jewsbury' 'Ruoyu Wang' 'Abhir Bhalerao' 'Nasir Rajpoot'\n 'Quoc Dang Vu']"
] |
null | null |
2403.09303
| null | null |
http://arxiv.org/pdf/2403.09303v3
|
2024-07-09T01:14:41Z
|
2024-03-14T11:51:01Z
|
Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical
Perspective
|
Medical anomaly detection aims to identify abnormal findings using only normal training data, playing a crucial role in health screening and recognizing rare diseases. Reconstruction-based methods, particularly those utilizing autoencoders (AEs), are dominant in this field. They work under the assumption that AEs trained on only normal data cannot reconstruct unseen abnormal regions well, thereby enabling the anomaly detection based on reconstruction errors. However, this assumption does not always hold due to the mismatch between the reconstruction training objective and the anomaly detection task objective, rendering these methods theoretically unsound. This study focuses on providing a theoretical foundation for AE-based reconstruction methods in anomaly detection. By leveraging information theory, we elucidate the principles of these methods and reveal that the key to improving AE in anomaly detection lies in minimizing the information entropy of latent vectors. Experiments on four datasets with two image modalities validate the effectiveness of our theory. To the best of our knowledge, this is the first effort to theoretically clarify the principles and design philosophy of AE for anomaly detection. The code is available at url{https://github.com/caiyu6666/AE4AD}.
|
[
"['Yu Cai' 'Hao Chen' 'Kwang-Ting Cheng']"
] |
null | null |
2403.09318
| null | null |
http://arxiv.org/pdf/2403.09318v1
|
2024-03-14T12:09:36Z
|
2024-03-14T12:09:36Z
|
A Hierarchical Fused Quantum Fuzzy Neural Network for Image
Classification
|
Neural network is a powerful learning paradigm for data feature learning in the era of big data. However, most neural network models are deterministic models that ignore the uncertainty of data. Fuzzy neural networks are proposed to address this problem. FDNN is a hierarchical deep neural network that derives information from both fuzzy and neural representations, the representations are then fused to form representation to be classified. FDNN perform well on uncertain data classification tasks. In this paper, we proposed a novel hierarchical fused quantum fuzzy neural network (HQFNN). Different from classical FDNN, HQFNN uses quantum neural networks to learn fuzzy membership functions in fuzzy neural network. We conducted simulated experiment on two types of datasets (Dirty-MNIST and 15-Scene), the results show that the proposed model can outperform several existing methods. In addition, we demonstrate the robustness of the proposed quantum circuit.
|
[
"['Sheng-Yao Wu' 'Run-Ze Li' 'Yan-Qi Song' 'Su-Juan Qin' 'Qiao-Yan Wen'\n 'Fei Gao']"
] |
null | null |
2403.09347
| null | null |
http://arxiv.org/pdf/2403.09347v4
|
2024-06-06T05:43:52Z
|
2024-03-14T12:51:58Z
|
BurstAttention: An Efficient Distributed Attention Framework for
Extremely Long Sequences
|
Effective attention modules have played a crucial role in the success of Transformer-based large language models (LLMs), but the quadratic time and memory complexities of these attention modules also pose a challenge when processing long sequences. One potential solution for the long sequence problem is to utilize distributed clusters to parallelize the computation of attention modules across multiple devices (e.g., GPUs). However, adopting a distributed approach inevitably introduces extra memory overheads to store local attention results and incurs additional communication costs to aggregate local results into global ones. In this paper, we propose a distributed attention framework named ``BurstAttention'' to optimize memory access and communication operations at both the global cluster and local device levels. In our experiments, we compare BurstAttention with other competitive distributed attention solutions for long sequence processing. The experimental results under different length settings demonstrate that BurstAttention offers significant advantages for processing long sequences compared with these competitive baselines, reducing 40% communication overheads and achieving 1.37 X speedup during training 128K sequence length on 32 X A100.
|
[
"['Ao Sun' 'Weilin Zhao' 'Xu Han' 'Cheng Yang' 'Zhiyuan Liu' 'Chuan Shi'\n 'Maosong Sun']"
] |
null | null |
2403.09383
| null | null |
http://arxiv.org/pdf/2403.09383v1
|
2024-03-14T13:34:30Z
|
2024-03-14T13:34:30Z
|
Pantypes: Diverse Representatives for Self-Explainable Models
|
Prototypical self-explainable classifiers have emerged to meet the growing demand for interpretable AI systems. These classifiers are designed to incorporate high transparency in their decisions by basing inference on similarity with learned prototypical objects. While these models are designed with diversity in mind, the learned prototypes often do not sufficiently represent all aspects of the input distribution, particularly those in low density regions. Such lack of sufficient data representation, known as representation bias, has been associated with various detrimental properties related to machine learning diversity and fairness. In light of this, we introduce pantypes, a new family of prototypical objects designed to capture the full diversity of the input distribution through a sparse set of objects. We show that pantypes can empower prototypical self-explainable models by occupying divergent regions of the latent space and thus fostering high diversity, interpretability and fairness.
|
[
"['Rune Kjærsgaard' 'Ahcène Boubekki' 'Line Clemmensen']"
] |
null | null |
2403.09389
| null | null |
http://arxiv.org/pdf/2403.09389v2
|
2024-06-03T09:10:27Z
|
2024-03-14T13:40:26Z
|
Learning to optimize with convergence guarantees using nonlinear system
theory
|
The increasing reliance on numerical methods for controlling dynamical systems and training machine learning models underscores the need to devise algorithms that dependably and efficiently navigate complex optimization landscapes. Classical gradient descent methods offer strong theoretical guarantees for convex problems; however, they demand meticulous hyperparameter tuning for non-convex ones. The emerging paradigm of learning to optimize (L2O) automates the discovery of algorithms with optimized performance leveraging learning models and data - yet, it lacks a theoretical framework to analyze convergence of the learned algorithms. In this paper, we fill this gap by harnessing nonlinear system theory. Specifically, we propose an unconstrained parametrization of all convergent algorithms for smooth non-convex objective functions. Notably, our framework is directly compatible with automatic differentiation tools, ensuring convergence by design while learning to optimize.
|
[
"['Andrea Martin' 'Luca Furieri']"
] |
null | null |
2403.09407
| null | null |
http://arxiv.org/pdf/2403.09407v1
|
2024-03-14T13:59:04Z
|
2024-03-14T13:59:04Z
|
LM2D: Lyrics- and Music-Driven Dance Synthesis
|
Dance typically involves professional choreography with complex movements that follow a musical rhythm and can also be influenced by lyrical content. The integration of lyrics in addition to the auditory dimension, enriches the foundational tone and makes motion generation more amenable to its semantic meanings. However, existing dance synthesis methods tend to model motions only conditioned on audio signals. In this work, we make two contributions to bridge this gap. First, we propose LM2D, a novel probabilistic architecture that incorporates a multimodal diffusion model with consistency distillation, designed to create dance conditioned on both music and lyrics in one diffusion generation step. Second, we introduce the first 3D dance-motion dataset that encompasses both music and lyrics, obtained with pose estimation technologies. We evaluate our model against music-only baseline models with objective metrics and human evaluations, including dancers and choreographers. The results demonstrate LM2D is able to produce realistic and diverse dance matching both lyrics and music. A video summary can be accessed at: https://youtu.be/4XCgvYookvA.
|
[
"['Wenjie Yin' 'Xuejiao Zhao' 'Yi Yu' 'Hang Yin' 'Danica Kragic'\n 'Mårten Björkman']"
] |
null | null |
2403.09415
| null | null |
http://arxiv.org/pdf/2403.09415v1
|
2024-03-14T14:04:37Z
|
2024-03-14T14:04:37Z
|
User Identification via Free Roaming Eye Tracking Data
|
We present a new dataset of "free roaming" (FR) and "targeted roaming" (TR): a pool of 41 participants is asked to walk around a university campus (FR) or is asked to find a particular room within a library (TR). Eye movements are recorded using a commodity wearable eye tracker (Pupil Labs Neon at 200Hz). On this dataset we investigate the accuracy of user identification using a previously known machine learning pipeline where a Radial Basis Function Network (RBFN) is used as classifier. Our highest accuracies are 87.3% for FR and 89.4% for TR. This should be compared to 95.3% which is the (corresponding) highest accuracy we are aware of (achieved in a laboratory setting using the "RAN" stimulus of the BioEye 2015 competition dataset). To the best of our knowledge, our results are the first that study user identification in a non laboratory setting; such settings are often more feasible than laboratory settings and may include further advantages. The minimum duration of each recording is 263s for FR and 154s for TR. Our best accuracies are obtained when restricting to 120s and 140s for FR and TR respectively, always cut from the end of the trajectories (both for the training and testing sessions). If we cut the same length from the beginning, then accuracies are 12.2% lower for FR and around 6.4% lower for TR. On the full trajectories accuracies are lower by 5% and 52% for FR and TR. We also investigate the impact of including higher order velocity derivatives (such as acceleration, jerk, or jounce).
|
[
"['Rishabh Vallabh Varsha Haria' 'Amin El Abed' 'Sebastian Maneth']"
] |
null | null |
2403.09428
| null | null |
http://arxiv.org/pdf/2403.09428v2
|
2024-03-26T17:38:38Z
|
2024-03-14T14:19:48Z
|
Borrowing Treasures from Neighbors: In-Context Learning for Multimodal
Learning with Missing Modalities and Data Scarcity
|
Multimodal machine learning with missing modalities is an increasingly relevant challenge arising in various applications such as healthcare. This paper extends the current research into missing modalities to the low-data regime, i.e., a downstream task has both missing modalities and limited sample size issues. This problem setting is particularly challenging and also practical as it is often expensive to get full-modality data and sufficient annotated training samples. We propose to use retrieval-augmented in-context learning to address these two crucial issues by unleashing the potential of a transformer's in-context learning ability. Diverging from existing methods, which primarily belong to the parametric paradigm and often require sufficient training samples, our work exploits the value of the available full-modality data, offering a novel perspective on resolving the challenge. The proposed data-dependent framework exhibits a higher degree of sample efficiency and is empirically demonstrated to enhance the classification model's performance on both full- and missing-modality data in the low-data regime across various multimodal learning tasks. When only 1% of the training data are available, our proposed method demonstrates an average improvement of 6.1% over a recent strong baseline across various datasets and missing states. Notably, our method also reduces the performance gap between full-modality and missing-modality data compared with the baseline.
|
[
"['Zhuo Zhi' 'Ziquan Liu' 'Moe Elbadawi' 'Adam Daneshmend' 'Mine Orlu'\n 'Abdul Basit' 'Andreas Demosthenous' 'Miguel Rodrigues']"
] |
null | null |
2403.09429
| null | null |
http://arxiv.org/pdf/2403.09429v2
|
2024-03-15T09:03:41Z
|
2024-03-14T14:20:22Z
|
VISA: Variational Inference with Sequential Sample-Average
Approximations
|
We present variational inference with sequential sample-average approximation (VISA), a method for approximate inference in computationally intensive models, such as those based on numerical simulations. VISA extends importance-weighted forward-KL variational inference by employing a sequence of sample-average approximations, which are considered valid inside a trust region. This makes it possible to reuse model evaluations across multiple gradient steps, thereby reducing computational cost. We perform experiments on high-dimensional Gaussians, Lotka-Volterra dynamics, and a Pickover attractor, which demonstrate that VISA can achieve comparable approximation accuracy to standard importance-weighted forward-KL variational inference with computational savings of a factor two or more for conservatively chosen learning rates.
|
[
"['Heiko Zimmermann' 'Christian A. Naesseth' 'Jan-Willem van de Meent']"
] |
null | null |
2403.09441
| null | null |
http://arxiv.org/pdf/2403.09441v1
|
2024-03-14T14:34:25Z
|
2024-03-14T14:34:25Z
|
Adversarial Fine-tuning of Compressed Neural Networks for Joint
Improvement of Robustness and Efficiency
|
As deep learning (DL) models are increasingly being integrated into our everyday lives, ensuring their safety by making them robust against adversarial attacks has become increasingly critical. DL models have been found to be susceptible to adversarial attacks which can be achieved by introducing small, targeted perturbations to disrupt the input data. Adversarial training has been presented as a mitigation strategy which can result in more robust models. This adversarial robustness comes with additional computational costs required to design adversarial attacks during training. The two objectives -- adversarial robustness and computational efficiency -- then appear to be in conflict of each other. In this work, we explore the effects of two different model compression methods -- structured weight pruning and quantization -- on adversarial robustness. We specifically explore the effects of fine-tuning on compressed models, and present the trade-off between standard fine-tuning and adversarial fine-tuning. Our results show that compression does not inherently lead to loss in model robustness and adversarial fine-tuning of a compressed model can yield large improvement to the robustness performance of models. We present experiments on two benchmark datasets showing that adversarial fine-tuning of compressed models can achieve robustness performance comparable to adversarially trained models, while also improving computational efficiency.
|
[
"['Hallgrimur Thorsteinsson' 'Valdemar J Henriksen' 'Tong Chen'\n 'Raghavendra Selvan']"
] |
null | null |
2403.09450
| null | null |
http://arxiv.org/pdf/2403.09450v2
|
2024-04-22T16:48:39Z
|
2024-03-14T14:48:37Z
|
Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative
Privacy Risk
|
While diffusion models have recently demonstrated remarkable progress in generating realistic images, privacy risks also arise: published models or APIs could generate training images and thus leak privacy-sensitive training information. In this paper, we reveal a new risk, Shake-to-Leak (S2L), that fine-tuning the pre-trained models with manipulated data can amplify the existing privacy risks. We demonstrate that S2L could occur in various standard fine-tuning strategies for diffusion models, including concept-injection methods (DreamBooth and Textual Inversion) and parameter-efficient methods (LoRA and Hypernetwork), as well as their combinations. In the worst case, S2L can amplify the state-of-the-art membership inference attack (MIA) on diffusion models by $5.4%$ (absolute difference) AUC and can increase extracted private samples from almost $0$ samples to $15.8$ samples on average per target domain. This discovery underscores that the privacy risk with diffusion models is even more severe than previously recognized. Codes are available at https://github.com/VITA-Group/Shake-to-Leak.
|
[
"['Zhangheng Li' 'Junyuan Hong' 'Bo Li' 'Zhangyang Wang']"
] |
null | null |
2403.09454
| null | null |
http://arxiv.org/abs/2403.09454v1
|
2024-03-14T14:53:18Z
|
2024-03-14T14:53:18Z
|
Machine learning for structural design models of continuous beam systems
via influence zones
|
This work develops a machine learned structural design model for continuous beam systems from the inverse problem perspective. After demarcating between forward, optimisation and inverse machine learned operators, the investigation proposes a novel methodology based on the recently developed influence zone concept which represents a fundamental shift in approach compared to traditional structural design methods. The aim of this approach is to conceptualise a non-iterative structural design model that predicts cross-section requirements for continuous beam systems of arbitrary system size. After generating a dataset of known solutions, an appropriate neural network architecture is identified, trained, and tested against unseen data. The results show a mean absolute percentage testing error of 1.6% for cross-section property predictions, along with a good ability of the neural network to generalise well to structural systems of variable size. The CBeamXP dataset generated in this work and an associated python-based neural network training script are available at an open-source data repository to allow for the reproducibility of results and to encourage further investigations.
|
[
"['Adrien Gallet' 'Andrew Liew' 'Iman Hajirasouliha' 'Danny Smyl']"
] |
null | null |
2403.09465
| null | null |
http://arxiv.org/pdf/2403.09465v1
|
2024-03-14T15:04:45Z
|
2024-03-14T15:04:45Z
|
Outlier Robust Multivariate Polynomial Regression
|
We study the problem of robust multivariate polynomial regression: let $pcolonmathbb{R}^ntomathbb{R}$ be an unknown $n$-variate polynomial of degree at most $d$ in each variable. We are given as input a set of random samples $(mathbf{x}_i,y_i) in [-1,1]^n times mathbb{R}$ that are noisy versions of $(mathbf{x}_i,p(mathbf{x}_i))$. More precisely, each $mathbf{x}_i$ is sampled independently from some distribution $chi$ on $[-1,1]^n$, and for each $i$ independently, $y_i$ is arbitrary (i.e., an outlier) with probability at most $rho < 1/2$, and otherwise satisfies $|y_i-p(mathbf{x}_i)|leqsigma$. The goal is to output a polynomial $hat{p}$, of degree at most $d$ in each variable, within an $ell_infty$-distance of at most $O(sigma)$ from $p$. Kane, Karmalkar, and Price [FOCS'17] solved this problem for $n=1$. We generalize their results to the $n$-variate setting, showing an algorithm that achieves a sample complexity of $O_n(d^nlog d)$, where the hidden constant depends on $n$, if $chi$ is the $n$-dimensional Chebyshev distribution. The sample complexity is $O_n(d^{2n}log d)$, if the samples are drawn from the uniform distribution instead. The approximation error is guaranteed to be at most $O(sigma)$, and the run-time depends on $log(1/sigma)$. In the setting where each $mathbf{x}_i$ and $y_i$ are known up to $N$ bits of precision, the run-time's dependence on $N$ is linear. We also show that our sample complexities are optimal in terms of $d^n$. Furthermore, we show that it is possible to have the run-time be independent of $1/sigma$, at the cost of a higher sample complexity.
|
[
"['Vipul Arora' 'Arnab Bhattacharyya' 'Mathews Boban'\n 'Venkatesan Guruswami' 'Esty Kelman']"
] |
null | null |
2403.09472
| null | null |
http://arxiv.org/pdf/2403.09472v1
|
2024-03-14T15:12:38Z
|
2024-03-14T15:12:38Z
|
Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision
|
Current AI alignment methodologies rely on human-provided demonstrations or judgments, and the learned capabilities of AI systems would be upper-bounded by human capabilities as a result. This raises a challenging research question: How can we keep improving the systems when their capabilities have surpassed the levels of humans? This paper answers this question in the context of tackling hard reasoning tasks (e.g., level 4-5 MATH problems) via learning from human annotations on easier tasks (e.g., level 1-3 MATH problems), which we term as textit{easy-to-hard generalization}. Our key insight is that an evaluator (reward model) trained on supervisions for easier tasks can be effectively used for scoring candidate solutions of harder tasks and hence facilitating easy-to-hard generalization over different levels of tasks. Based on this insight, we propose a novel approach to scalable alignment, which firstly trains the process-supervised reward models on easy problems (e.g., level 1-3), and then uses them to evaluate the performance of policy models on hard problems. We show that such textit{easy-to-hard generalization from evaluators} can enable textit{easy-to-hard generalizations in generators} either through re-ranking or reinforcement learning (RL). Notably, our process-supervised 7b RL model achieves an accuracy of 34.0% on MATH500, despite only using human supervision on easy problems. Our approach suggests a promising path toward AI systems that advance beyond the frontier of human supervision.
|
[
"['Zhiqing Sun' 'Longhui Yu' 'Yikang Shen' 'Weiyang Liu' 'Yiming Yang'\n 'Sean Welleck' 'Chuang Gan']"
] |
null | null |
2403.09477
| null | null |
http://arxiv.org/pdf/2403.09477v1
|
2024-03-14T15:19:19Z
|
2024-03-14T15:19:19Z
|
VIRUS-NeRF -- Vision, InfraRed and UltraSonic based Neural Radiance
Fields
|
Autonomous mobile robots are an increasingly integral part of modern factory and warehouse operations. Obstacle detection, avoidance and path planning are critical safety-relevant tasks, which are often solved using expensive LiDAR sensors and depth cameras. We propose to use cost-effective low-resolution ranging sensors, such as ultrasonic and infrared time-of-flight sensors by developing VIRUS-NeRF - Vision, InfraRed, and UltraSonic based Neural Radiance Fields. Building upon Instant Neural Graphics Primitives with a Multiresolution Hash Encoding (Instant-NGP), VIRUS-NeRF incorporates depth measurements from ultrasonic and infrared sensors and utilizes them to update the occupancy grid used for ray marching. Experimental evaluation in 2D demonstrates that VIRUS-NeRF achieves comparable mapping performance to LiDAR point clouds regarding coverage. Notably, in small environments, its accuracy aligns with that of LiDAR measurements, while in larger ones, it is bounded by the utilized ultrasonic sensors. An in-depth ablation study reveals that adding ultrasonic and infrared sensors is highly effective when dealing with sparse data and low view variation. Further, the proposed occupancy grid of VIRUS-NeRF improves the mapping capabilities and increases the training speed by 46% compared to Instant-NGP. Overall, VIRUS-NeRF presents a promising approach for cost-effective local mapping in mobile robotics, with potential applications in safety and navigation tasks. The code can be found at https://github.com/ethz-asl/virus nerf.
|
[
"['Nicolaj Schmid' 'Cornelius von Einem' 'Cesar Cadena' 'Roland Siegwart'\n 'Lorenz Hruby' 'Florian Tschopp']"
] |
null | null |
2403.09479
| null | null |
http://arxiv.org/pdf/2403.09479v1
|
2024-03-14T15:20:54Z
|
2024-03-14T15:20:54Z
|
Laying the Foundation First? Investigating the Generalization from
Atomic Skills to Complex Reasoning Tasks
|
Current language models have demonstrated their capability to develop basic reasoning, but struggle in more complicated reasoning tasks that require a combination of atomic skills, such as math word problem requiring skills like arithmetic and unit conversion. Previous methods either do not improve the inherent atomic skills of models or not attempt to generalize the atomic skills to complex reasoning tasks. In this paper, we first propose a probing framework to investigate whether the atomic skill can spontaneously generalize to complex reasoning tasks. Then, we introduce a hierarchical curriculum learning training strategy to achieve better skill generalization. In our experiments, we find that atomic skills can not spontaneously generalize to compositional tasks. By leveraging hierarchical curriculum learning, we successfully induce generalization, significantly improve the performance of open-source LMs on complex reasoning tasks. Promisingly, the skill generalization exhibit effective in cross-dataset and cross-domain scenarios. Complex reasoning can also help enhance atomic skills. Our findings offer valuable guidance for designing better training strategies for complex reasoning tasks.
|
[
"['Yuncheng Huang' 'Qianyu He' 'Yipei Xu' 'Jiaqing Liang' 'Yanghua Xiao']"
] |
null | null |
2403.09491
| null | null |
http://arxiv.org/pdf/2403.09491v1
|
2024-03-14T15:32:25Z
|
2024-03-14T15:32:25Z
|
On using Machine Learning Algorithms for Motorcycle Collision Detection
|
Globally, motorcycles attract vast and varied users. However, since the rate of severe injury and fatality in motorcycle accidents far exceeds passenger car accidents, efforts have been directed toward increasing passive safety systems. Impact simulations show that the risk of severe injury or death in the event of a motorcycle-to-car impact can be greatly reduced if the motorcycle is equipped with passive safety measures such as airbags and seat belts. For the passive safety systems to be activated, a collision must be detected within milliseconds for a wide variety of impact configurations, but under no circumstances may it be falsely triggered. For the challenge of reliably detecting impending collisions, this paper presents an investigation towards the applicability of machine learning algorithms. First, a series of simulations of accidents and driving operation is introduced to collect data to train machine learning classification models. Their performance is henceforth assessed and compared via multiple representative and application-oriented criteria.
|
[
"['Philipp Rodegast' 'Steffen Maier' 'Jonas Kneifl' 'Jörg Fehr']"
] |
null | null |
2403.09499
| null | null |
http://arxiv.org/pdf/2403.09499v3
|
2024-05-15T17:11:35Z
|
2024-03-14T15:42:26Z
|
A Reinforcement Learning Approach to Dairy Farm Battery Management using
Q Learning
|
Dairy farming consumes a significant amount of energy, making it an energy-intensive sector within agriculture. Integrating renewable energy generation into dairy farming could help address this challenge. Effective battery management is important for integrating renewable energy generation. Managing battery charging and discharging poses significant challenges because of fluctuations in electrical consumption, the intermittent nature of renewable energy generation, and fluctuations in energy prices. Artificial Intelligence (AI) has the potential to significantly improve the use of renewable energy in dairy farming, however, there is limited research conducted in this particular domain. This research considers Ireland as a case study as it works towards attaining its 2030 energy strategy centered on the utilization of renewable sources. This study proposes a Q-learning-based algorithm for scheduling battery charging and discharging in a dairy farm setting. This research also explores the effect of the proposed algorithm by adding wind generation data and considering additional case studies. The proposed algorithm reduces the cost of imported electricity from the grid by 13.41%, peak demand by 2%, and 24.49% when utilizing wind generation. These results underline how reinforcement learning is highly effective in managing batteries in the dairy farming sector.
|
[
"['Nawazish Ali' 'Abdul Wahid' 'Rachael Shaw' 'Karl Mason']"
] |
null | null |
2403.09502
| null | null |
http://arxiv.org/pdf/2403.09502v2
|
2024-06-20T06:23:16Z
|
2024-03-14T15:44:19Z
|
EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning
|
Recent advancements in self-supervised audio-visual representation learning have demonstrated its potential to capture rich and comprehensive representations. However, despite the advantages of data augmentation verified in many learning methods, audio-visual learning has struggled to fully harness these benefits, as augmentations can easily disrupt the correspondence between input pairs. To address this limitation, we introduce EquiAV, a novel framework that leverages equivariance for audio-visual contrastive learning. Our approach begins with extending equivariance to audio-visual learning, facilitated by a shared attention-based transformation predictor. It enables the aggregation of features from diverse augmentations into a representative embedding, providing robust supervision. Notably, this is achieved with minimal computational overhead. Extensive ablation studies and qualitative results verify the effectiveness of our method. EquiAV outperforms previous works across various audio-visual benchmarks. The code is available on https://github.com/JongSuk1/EquiAV.
|
[
"['Jongsuk Kim' 'Hyeongkeun Lee' 'Kyeongha Rho' 'Junmo Kim'\n 'Joon Son Chung']"
] |
null | null |
2403.09506
| null | null |
http://arxiv.org/pdf/2403.09506v2
|
2024-03-25T02:45:35Z
|
2024-03-14T15:53:04Z
|
Don't Judge by the Look: Towards Motion Coherent Video Representation
|
Current training pipelines in object recognition neglect Hue Jittering when doing data augmentation as it not only brings appearance changes that are detrimental to classification, but also the implementation is inefficient in practice. In this study, we investigate the effect of hue variance in the context of video understanding and find this variance to be beneficial since static appearances are less important in videos that contain motion information. Based on this observation, we propose a data augmentation method for video understanding, named Motion Coherent Augmentation (MCA), that introduces appearance variation in videos and implicitly encourages the model to prioritize motion patterns, rather than static appearances. Concretely, we propose an operation SwapMix to efficiently modify the appearance of video samples, and introduce Variation Alignment (VA) to resolve the distribution shift caused by SwapMix, enforcing the model to learn appearance invariant representations. Comprehensive empirical evaluation across various architectures and different datasets solidly validates the effectiveness and generalization ability of MCA, and the application of VA in other augmentation methods. Code is available at https://github.com/BeSpontaneous/MCA-pytorch.
|
[
"['Yitian Zhang' 'Yue Bai' 'Huan Wang' 'Yizhou Wang' 'Yun Fu']"
] |
null | null |
2403.09509
| null | null |
http://arxiv.org/abs/2403.09509v1
|
2024-03-14T15:56:02Z
|
2024-03-14T15:56:02Z
|
On STPA for Distributed Development of Safe Autonomous Driving: An
Interview Study
|
Safety analysis is used to identify hazards and build knowledge during the design phase of safety-relevant functions. This is especially true for complex AI-enabled and software intensive systems such as Autonomous Drive (AD). System-Theoretic Process Analysis (STPA) is a novel method applied in safety-related fields like defense and aerospace, which is also becoming popular in the automotive industry. However, STPA assumes prerequisites that are not fully valid in the automotive system engineering with distributed system development and multi-abstraction design levels. This would inhibit software developers from using STPA to analyze their software as part of a bigger system, resulting in a lack of traceability. This can be seen as a maintainability challenge in continuous development and deployment (DevOps). In this paper, STPA's different guidelines for the automotive industry, e.g. J31887/ISO21448/STPA handbook, are firstly compared to assess their applicability to the distributed development of complex AI-enabled systems like AD. Further, an approach to overcome the challenges of using STPA in a multi-level design context is proposed. By conducting an interview study with automotive industry experts for the development of AD, the challenges are validated and the effectiveness of the proposed approach is evaluated.
|
[
"['Ali Nouri' 'Christian Berger' 'Fredrik Törner']"
] |
null | null |
2403.09516
| null | null |
http://arxiv.org/pdf/2403.09516v3
|
2024-04-05T18:35:37Z
|
2024-03-14T15:58:36Z
|
Leveraging Prototypical Representations for Mitigating Social Bias
without Demographic Information
|
Mitigating social biases typically requires identifying the social groups associated with each data sample. In this paper, we present DAFair, a novel approach to address social bias in language models. Unlike traditional methods that rely on explicit demographic labels, our approach does not require any such information. Instead, we leverage predefined prototypical demographic texts and incorporate a regularization term during the fine-tuning process to mitigate bias in the model's representations. Our empirical results across two tasks and two models demonstrate the effectiveness of our method compared to previous approaches that do not rely on labeled data. Moreover, with limited demographic-annotated data, our approach outperforms common debiasing approaches.
|
[
"['Shadi Iskander' 'Kira Radinsky' 'Yonatan Belinkov']"
] |
null | null |
2403.09539
| null | null |
http://arxiv.org/pdf/2403.09539v2
|
2024-03-15T02:07:30Z
|
2024-03-14T16:27:49Z
|
Logits of API-Protected LLMs Leak Proprietary Information
|
The commercialization of large language models (LLMs) has led to the common practice of high-level API-only access to proprietary models. In this work, we show that even with a conservative assumption about the model architecture, it is possible to learn a surprisingly large amount of non-public information about an API-protected LLM from a relatively small number of API queries (e.g., costing under $1,000 for OpenAI's gpt-3.5-turbo). Our findings are centered on one key observation: most modern LLMs suffer from a softmax bottleneck, which restricts the model outputs to a linear subspace of the full output space. We show that this lends itself to a model image or a model signature which unlocks several capabilities with affordable cost: efficiently discovering the LLM's hidden size, obtaining full-vocabulary outputs, detecting and disambiguating different model updates, identifying the source LLM given a single full LLM output, and even estimating the output layer parameters. Our empirical investigations show the effectiveness of our methods, which allow us to estimate the embedding size of OpenAI's gpt-3.5-turbo to be about 4,096. Lastly, we discuss ways that LLM providers can guard against these attacks, as well as how these capabilities can be viewed as a feature (rather than a bug) by allowing for greater transparency and accountability.
|
[
"['Matthew Finlayson' 'Xiang Ren' 'Swabha Swayamdipta']"
] |
null | null |
2403.09543
| null | null |
http://arxiv.org/pdf/2403.09543v1
|
2024-03-14T16:30:52Z
|
2024-03-14T16:30:52Z
|
Explorations in Texture Learning
|
In this work, we investigate textit{texture learning}: the identification of textures learned by object classification models, and the extent to which they rely on these textures. We build texture-object associations that uncover new insights about the relationships between texture and object classes in CNNs and find three classes of results: associations that are strong and expected, strong and not expected, and expected but not present. Our analysis demonstrates that investigations in texture learning enable new methods for interpretability and have the potential to uncover unexpected biases.
|
[
"['Blaine Hoak' 'Patrick McDaniel']"
] |
null | null |
2403.09547
| null | null |
http://arxiv.org/pdf/2403.09547v1
|
2024-03-14T16:35:39Z
|
2024-03-14T16:35:39Z
|
How do Machine Learning Projects use Continuous Integration Practices?
An Empirical Study on GitHub Actions
|
Continuous Integration (CI) is a well-established practice in traditional software development, but its nuances in the domain of Machine Learning (ML) projects remain relatively unexplored. Given the distinctive nature of ML development, understanding how CI practices are adopted in this context is crucial for tailoring effective approaches. In this study, we conduct a comprehensive analysis of 185 open-source projects on GitHub (93 ML and 92 non-ML projects). Our investigation comprises both quantitative and qualitative dimensions, aiming to uncover differences in CI adoption between ML and non-ML projects. Our findings indicate that ML projects often require longer build durations, and medium-sized ML projects exhibit lower test coverage compared to non-ML projects. Moreover, small and medium-sized ML projects show a higher prevalence of increasing build duration trends compared to their non-ML counterparts. Additionally, our qualitative analysis illuminates the discussions around CI in both ML and non-ML projects, encompassing themes like CI Build Execution and Status, CI Testing, and CI Infrastructure. These insights shed light on the unique challenges faced by ML projects in adopting CI practices effectively.
|
[
"['João Helis Bernardo' 'Daniel Alencar da Costa'\n 'Sérgio Queiroz de Medeiros' 'Uirá Kulesza']"
] |
null | null |
2403.09548
| null | null |
http://arxiv.org/pdf/2403.09548v1
|
2024-03-14T16:35:43Z
|
2024-03-14T16:35:43Z
|
Breast Cancer Classification Using Gradient Boosting Algorithms Focusing
on Reducing the False Negative and SHAP for Explainability
|
Cancer is one of the diseases that kill the most women in the world, with breast cancer being responsible for the highest number of cancer cases and consequently deaths. However, it can be prevented by early detection and, consequently, early treatment. Any development for detection or perdition this kind of cancer is important for a better healthy life. Many studies focus on a model with high accuracy in cancer prediction, but sometimes accuracy alone may not always be a reliable metric. This study implies an investigative approach to studying the performance of different machine learning algorithms based on boosting to predict breast cancer focusing on the recall metric. Boosting machine learning algorithms has been proven to be an effective tool for detecting medical diseases. The dataset of the University of California, Irvine (UCI) repository has been utilized to train and test the model classifier that contains their attributes. The main objective of this study is to use state-of-the-art boosting algorithms such as AdaBoost, XGBoost, CatBoost and LightGBM to predict and diagnose breast cancer and to find the most effective metric regarding recall, ROC-AUC, and confusion matrix. Furthermore, our study is the first to use these four boosting algorithms with Optuna, a library for hyperparameter optimization, and the SHAP method to improve the interpretability of our model, which can be used as a support to identify and predict breast cancer. We were able to improve AUC or recall for all the models and reduce the False Negative for AdaBoost and LigthGBM the final AUC were more than 99.41% for all models.
|
[
"['João Manoel Herrera Pinheiro' 'Marcelo Becker']"
] |
null | null |
2403.09549
| null | null |
http://arxiv.org/pdf/2403.09549v1
|
2024-03-14T16:38:02Z
|
2024-03-14T16:38:02Z
|
Generalizing Denoising to Non-Equilibrium Structures Improves
Equivariant Force Fields
|
Understanding the interactions of atoms such as forces in 3D atomistic systems is fundamental to many applications like molecular dynamics and catalyst design. However, simulating these interactions requires compute-intensive ab initio calculations and thus results in limited data for training neural networks. In this paper, we propose to use denoising non-equilibrium structures (DeNS) as an auxiliary task to better leverage training data and improve performance. For training with DeNS, we first corrupt a 3D structure by adding noise to its 3D coordinates and then predict the noise. Different from previous works on denoising, which are limited to equilibrium structures, the proposed method generalizes denoising to a much larger set of non-equilibrium structures. The main difference is that a non-equilibrium structure does not correspond to local energy minima and has non-zero forces, and therefore it can have many possible atomic positions compared to an equilibrium structure. This makes denoising non-equilibrium structures an ill-posed problem since the target of denoising is not uniquely defined. Our key insight is to additionally encode the forces of the original non-equilibrium structure to specify which non-equilibrium structure we are denoising. Concretely, given a corrupted non-equilibrium structure and the forces of the original one, we predict the non-equilibrium structure satisfying the input forces instead of any arbitrary structures. Since DeNS requires encoding forces, DeNS favors equivariant networks, which can easily incorporate forces and other higher-order tensors in node embeddings. We study the effectiveness of training equivariant networks with DeNS on OC20, OC22 and MD17 datasets and demonstrate that DeNS can achieve new state-of-the-art results on OC20 and OC22 and significantly improve training efficiency on MD17.
|
[
"['Yi-Lun Liao' 'Tess Smidt' 'Abhishek Das']"
] |
null | null |
2403.09560
| null | null |
http://arxiv.org/pdf/2403.09560v2
|
2024-06-05T07:46:36Z
|
2024-03-14T16:52:57Z
|
Self-Consistency Training for Density-Functional-Theory Hamiltonian
Prediction
|
Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental formulation to leverage machine learning for solving molecular science problems. Yet, its applicability is limited by insufficient labeled data for training. In this work, we highlight that Hamiltonian prediction possesses a self-consistency principle, based on which we propose self-consistency training, an exact training method that does not require labeled data. It distinguishes the task from predicting other molecular properties by the following benefits: (1) it enables the model to be trained on a large amount of unlabeled data, hence addresses the data scarcity challenge and enhances generalization; (2) it is more efficient than running DFT to generate labels for supervised training, since it amortizes DFT calculation over a set of queries. We empirically demonstrate the better generalization in data-scarce and out-of-distribution scenarios, and the better efficiency over DFT labeling. These benefits push forward the applicability of Hamiltonian prediction to an ever-larger scale.
|
[
"['He Zhang' 'Chang Liu' 'Zun Wang' 'Xinran Wei' 'Siyuan Liu'\n 'Nanning Zheng' 'Bin Shao' 'Tie-Yan Liu']"
] |
null | null |
2403.09570
| null | null |
http://arxiv.org/pdf/2403.09570v2
|
2024-04-24T16:37:02Z
|
2024-03-14T17:00:01Z
|
Multi-Fidelity Bayesian Optimization With Across-Task Transferable
Max-Value Entropy Search
|
In many applications, ranging from logistics to engineering, a designer is faced with a sequence of optimization tasks for which the objectives are in the form of black-box functions that are costly to evaluate. For example, the designer may need to tune the hyperparameters of neural network models for different learning tasks over time. Rather than evaluating the objective function for each candidate solution, the designer may have access to approximations of the objective functions, for which higher-fidelity evaluations entail a larger cost. Existing multi-fidelity black-box optimization strategies select candidate solutions and fidelity levels with the goal of maximizing the information accrued about the optimal value or solution for the current task. Assuming that successive optimization tasks are related, this paper introduces a novel information-theoretic acquisition function that balances the need to acquire information about the current task with the goal of collecting information transferable to future tasks. The proposed method includes shared inter-task latent variables, which are transferred across tasks by implementing particle-based variational Bayesian updates. Experimental results across synthetic and real-world examples reveal that the proposed provident acquisition strategy that caters to future tasks can significantly improve the optimization efficiency as soon as a sufficient number of tasks is processed.
|
[
"['Yunchuan Zhang' 'Sangwoo Park' 'Osvaldo Simeone']"
] |
null | null |
2403.09571
| null | null |
http://arxiv.org/pdf/2403.09571v1
|
2024-03-14T17:00:29Z
|
2024-03-14T17:00:29Z
|
Are you a robot? Detecting Autonomous Vehicles from Behavior Analysis
|
The tremendous hype around autonomous driving is eagerly calling for emerging and novel technologies to support advanced mobility use cases. As car manufactures keep developing SAE level 3+ systems to improve the safety and comfort of passengers, traffic authorities need to establish new procedures to manage the transition from human-driven to fully-autonomous vehicles while providing a feedback-loop mechanism to fine-tune envisioned autonomous systems. Thus, a way to automatically profile autonomous vehicles and differentiate those from human-driven ones is a must. In this paper, we present a fully-fledged framework that monitors active vehicles using camera images and state information in order to determine whether vehicles are autonomous, without requiring any active notification from the vehicles themselves. Essentially, it builds on the cooperation among vehicles, which share their data acquired on the road feeding a machine learning model to identify autonomous cars. We extensively tested our solution and created the NexusStreet dataset, by means of the CARLA simulator, employing an autonomous driving control agent and a steering wheel maneuvered by licensed drivers. Experiments show it is possible to discriminate the two behaviors by analyzing video clips with an accuracy of 80%, which improves up to 93% when the target state information is available. Lastly, we deliberately degraded the state to observe how the framework performs under non-ideal data collection conditions.
|
[
"['Fabio Maresca' 'Filippo Grazioli' 'Antonio Albanese'\n 'Vincenzo Sciancalepore' 'Gianpiero Negri' 'Xavier Costa-Perez']"
] |
null | null |
2403.09579
| null | null |
http://arxiv.org/pdf/2403.09579v1
|
2024-03-14T17:13:37Z
|
2024-03-14T17:13:37Z
|
uaMix-MAE: Efficient Tuning of Pretrained Audio Transformers with
Unsupervised Audio Mixtures
|
Masked Autoencoders (MAEs) learn rich low-level representations from unlabeled data but require substantial labeled data to effectively adapt to downstream tasks. Conversely, Instance Discrimination (ID) emphasizes high-level semantics, offering a potential solution to alleviate annotation requirements in MAEs. Although combining these two approaches can address downstream tasks with limited labeled data, naively integrating ID into MAEs leads to extended training times and high computational costs. To address this challenge, we introduce uaMix-MAE, an efficient ID tuning strategy that leverages unsupervised audio mixtures. Utilizing contrastive tuning, uaMix-MAE aligns the representations of pretrained MAEs, thereby facilitating effective adaptation to task-specific semantics. To optimize the model with small amounts of unlabeled data, we propose an audio mixing technique that manipulates audio samples in both input and virtual label spaces. Experiments in low/few-shot settings demonstrate that modelname achieves 4-6% accuracy improvements over various benchmarks when tuned with limited unlabeled data, such as AudioSet-20K. Code is available at https://github.com/PLAN-Lab/uamix-MAE
|
[
"['Afrina Tabassum' 'Dung Tran' 'Trung Dang' 'Ismini Lourentzou'\n 'Kazuhito Koishida']"
] |
null | null |
2403.09580
| null | null |
http://arxiv.org/pdf/2403.09580v1
|
2024-03-14T17:14:53Z
|
2024-03-14T17:14:53Z
|
Algorithmic syntactic causal identification
|
Causal identification in causal Bayes nets (CBNs) is an important tool in causal inference allowing the derivation of interventional distributions from observational distributions where this is possible in principle. However, most existing formulations of causal identification using techniques such as d-separation and do-calculus are expressed within the mathematical language of classical probability theory on CBNs. However, there are many causal settings where probability theory and hence current causal identification techniques are inapplicable such as relational databases, dataflow programs such as hardware description languages, distributed systems and most modern machine learning algorithms. We show that this restriction can be lifted by replacing the use of classical probability theory with the alternative axiomatic foundation of symmetric monoidal categories. In this alternative axiomatization, we show how an unambiguous and clean distinction can be drawn between the general syntax of causal models and any specific semantic implementation of that causal model. This allows a purely syntactic algorithmic description of general causal identification by a translation of recent formulations of the general ID algorithm through fixing. Our description is given entirely in terms of the non-parametric ADMG structure specifying a causal model and the algebraic signature of the corresponding monoidal category, to which a sequence of manipulations is then applied so as to arrive at a modified monoidal category in which the desired, purely syntactic interventional causal model, is obtained. We use this idea to derive purely syntactic analogues of classical back-door and front-door causal adjustment, and illustrate an application to a more complex causal model.
|
[
"['Dhurim Cakiqi' 'Max A. Little']"
] |
null | null |
2403.09588
| null | null |
http://arxiv.org/pdf/2403.09588v1
|
2024-03-14T17:26:00Z
|
2024-03-14T17:26:00Z
|
Iterative Forgetting: Online Data Stream Regression Using
Database-Inspired Adaptive Granulation
|
Many modern systems, such as financial, transportation, and telecommunications systems, are time-sensitive in the sense that they demand low-latency predictions for real-time decision-making. Such systems often have to contend with continuous unbounded data streams as well as concept drift, which are challenging requirements that traditional regression techniques are unable to cater to. There exists a need to create novel data stream regression methods that can handle these scenarios. We present a database-inspired datastream regression model that (a) uses inspiration from R*-trees to create granules from incoming datastreams such that relevant information is retained, (b) iteratively forgets granules whose information is deemed to be outdated, thus maintaining a list of only recent, relevant granules, and (c) uses the recent data and granules to provide low-latency predictions. The R*-tree-inspired approach also makes the algorithm amenable to integration with database systems. Our experiments demonstrate that the ability of this method to discard data produces a significant order-of-magnitude improvement in latency and training time when evaluated against the most accurate state-of-the-art algorithms, while the R*-tree-inspired granulation technique provides competitively accurate predictions
|
[
"['Niket Kathiriya' 'Hossein Haeri' 'Cindy Chen' 'Kshitij Jerath']"
] |
null | null |
2403.09598
| null | null |
http://arxiv.org/pdf/2403.09598v2
|
2024-06-21T20:09:05Z
|
2024-03-14T17:39:14Z
|
Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds
|
Multi-label imbalanced classification poses a significant challenge in machine learning, particularly evident in bioacoustics where animal sounds often co-occur, and certain sounds are much less frequent than others. This paper focuses on the specific case of classifying anuran species sounds using the dataset AnuraSet, that contains both class imbalance and multi-label examples. To address these challenges, we introduce Mixture of Mixups (Mix2), a framework that leverages mixing regularization methods Mixup, Manifold Mixup, and MultiMix. Experimental results show that these methods, individually, may lead to suboptimal results; however, when applied randomly, with one selected at each training iteration, they prove effective in addressing the mentioned challenges, particularly for rare classes with few occurrences. Further analysis reveals that Mix2 is also proficient in classifying sounds across various levels of class co-occurrences.
|
[
"['Ilyass Moummad' 'Nicolas Farrugia' 'Romain Serizel' 'Jeremy Froidevaux'\n 'Vincent Lostanlen']"
] |
null | null |
2403.09603
| null | null |
http://arxiv.org/pdf/2403.09603v2
|
2024-03-16T08:51:56Z
|
2024-03-14T17:44:35Z
|
Optimistic Verifiable Training by Controlling Hardware Nondeterminism
|
The increasing compute demands of AI systems has led to the emergence of services that train models on behalf of clients lacking necessary resources. However, ensuring correctness of training and guarding against potential training-time attacks, such as data poisoning, poses challenges. Existing works on verifiable training largely fall into two classes: proof-based systems, which struggle to scale due to requiring cryptographic techniques, and "optimistic" methods that consider a trusted third-party auditor who replicates the training process. A key challenge with the latter is that hardware nondeterminism between GPU types during training prevents an auditor from replicating the training process exactly, and such schemes are therefore non-robust. We propose a method that combines training in a higher precision than the target model, rounding after intermediate computation steps, and storing rounding decisions based on an adaptive thresholding procedure, to successfully control for nondeterminism. Across three different NVIDIA GPUs (A40, Titan XP, RTX 2080 Ti), we achieve exact training replication at FP32 precision for both full-training and fine-tuning of ResNet-50 (23M) and GPT-2 (117M) models. Our verifiable training scheme significantly decreases the storage and time costs compared to proof-based systems.
|
[
"['Megha Srivastava' 'Simran Arora' 'Dan Boneh']"
] |
null | null |
2403.09611
| null | null |
http://arxiv.org/pdf/2403.09611v4
|
2024-04-18T18:51:04Z
|
2024-03-14T17:51:32Z
|
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
|
In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons. For example, we demonstrate that for large-scale multimodal pre-training using a careful mix of image-caption, interleaved image-text, and text-only data is crucial for achieving state-of-the-art (SOTA) few-shot results across multiple benchmarks, compared to other published pre-training results. Further, we show that the image encoder together with image resolution and the image token count has substantial impact, while the vision-language connector design is of comparatively negligible importance. By scaling up the presented recipe, we build MM1, a family of multimodal models up to 30B parameters, including both dense models and mixture-of-experts (MoE) variants, that are SOTA in pre-training metrics and achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks. Thanks to large-scale pre-training, MM1 enjoys appealing properties such as enhanced in-context learning, and multi-image reasoning, enabling few-shot chain-of-thought prompting.
|
[
"['Brandon McKinzie' 'Zhe Gan' 'Jean-Philippe Fauconnier' 'Sam Dodge'\n 'Bowen Zhang' 'Philipp Dufter' 'Dhruti Shah' 'Xianzhi Du' 'Futang Peng'\n 'Floris Weers' 'Anton Belyi' 'Haotian Zhang' 'Karanjeet Singh'\n 'Doug Kang' 'Ankur Jain' 'Hongyu Hè' 'Max Schwarzer' 'Tom Gunter'\n 'Xiang Kong' 'Aonan Zhang' 'Jianyu Wang' 'Chong Wang' 'Nan Du' 'Tao Lei'\n 'Sam Wiseman' 'Guoli Yin' 'Mark Lee' 'Zirui Wang' 'Ruoming Pang'\n 'Peter Grasch' 'Alexander Toshev' 'Yinfei Yang']"
] |
null | null |
2403.09612
| null | null |
http://arxiv.org/pdf/2403.09612v1
|
2024-03-14T17:51:38Z
|
2024-03-14T17:51:38Z
|
Compute-first optical detection for noise-resilient visual perception
|
In the context of visual perception, the optical signal from a scene is transferred into the electronic domain by detectors in the form of image data, which are then processed for the extraction of visual information. In noisy and weak-signal environments such as thermal imaging for night vision applications, however, the performance of neural computing tasks faces a significant bottleneck due to the inherent degradation of data quality upon noisy detection. Here, we propose a concept of optical signal processing before detection to address this issue. We demonstrate that spatially redistributing optical signals through a properly designed linear transformer can enhance the detection noise resilience of visual perception tasks, as benchmarked with the MNIST classification. Our idea is supported by a quantitative analysis detailing the relationship between signal concentration and noise robustness, as well as its practical implementation in an incoherent imaging system. This compute-first detection scheme can pave the way for advancing infrared machine vision technologies widely used for industrial and defense applications.
|
[
"['Jungmin Kim' 'Nanfang Yu' 'Zongfu Yu']"
] |
null | null |
2403.09613
| null | null |
http://arxiv.org/pdf/2403.09613v1
|
2024-03-14T17:51:54Z
|
2024-03-14T17:51:54Z
|
Reawakening knowledge: Anticipatory recovery from catastrophic
interference via structured training
|
We explore the training dynamics of neural networks in a structured non-IID setting where documents are presented cyclically in a fixed, repeated sequence. Typically, networks suffer from catastrophic interference when training on a sequence of documents; however, we discover a curious and remarkable property of LLMs fine-tuned sequentially in this setting: they exhibit anticipatory behavior, recovering from the forgetting on documents before encountering them again. The behavior emerges and becomes more robust as the architecture scales up its number of parameters. Through comprehensive experiments and visualizations, we uncover new insights into training over-parameterized networks in structured environments.
|
[
"['Yanlai Yang' 'Matt Jones' 'Michael C. Mozer' 'Mengye Ren']"
] |
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