Correctness Verification of Neural Networks Approximating Differential Equations
Verification of Neural Networks (NNs) that approximate the solution of Partial Differential Equations (PDEs) is a major milestone towards enhancing their trustworthiness and accelerating their...
View ArticleGlobal optimality under amenable symmetry constraints
We ask whether there exists a function or measure that (1) minimizes a given convex functional or risk and (2) satisfies a symmetry property specified by an amenable group of transformations. Examples...
View ArticleComparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2...
Medical image analysis frequently encounters data scarcity challenges. Transfer learning has been effective in addressing this issue while conserving computational resources. The recent advent of...
View ArticleRethinking Scaling Laws for Learning in Strategic Environments
The deployment of ever-larger machine learning models reflects a growing consensus that the more expressive the model$\unicode{x2013}$and the more data one has access to$\unicode{x2013}$the more one...
View ArticleA Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for...
Analog In-Memory Computing (AIMC) is an emerging technology for fast and energy-efficient Deep Learning (DL) inference. However, a certain amount of digital post-processing is required to deal with...
View ArticleShow Me How It's Done: The Role of Explanations in Fine-Tuning Language Models
Our research demonstrates the significant benefits of using fine-tuning with explanations to enhance the performance of language models. Unlike prompting, which maintains the model's parameters,...
View ArticleAccelerating Distributed Deep Learning using Lossless Homomorphic Compression
As deep neural networks (DNNs) grow in complexity and size, the resultant increase in communication overhead during distributed training has become a significant bottleneck, challenging the scalability...
View ArticleConvolutional Neural Networks for signal detection in real LIGO data
Searching the data of gravitational-wave detectors for signals from compact binary mergers is a computationally demanding task. Recently, machine learning algorithms have been proposed to address...
View ArticleCartesian atomic cluster expansion for machine learning interatomic potentials
Machine learning interatomic potentials are revolutionizing large-scale, accurate atomistic modelling in material science and chemistry. These potentials often use atomic cluster expansion or...
View ArticleA Hormetic Approach to the Value-Loading Problem: Preventing the Paperclip...
The value-loading problem is a significant challenge for researchers aiming to create artificial intelligence (AI) systems that align with human values and preferences. This problem requires a method...
View ArticleTriAug: Out-of-Distribution Detection for Robust Classification of Imbalanced...
Different diseases, such as histological subtypes of breast lesions, have severely varying incidence rates. Even trained with substantial amount of in-distribution (ID) data, models often encounter...
View ArticleAraSpider: Democratizing Arabic-to-SQL
This study presents AraSpider, the first Arabic version of the Spider dataset, aimed at improving natural language processing (NLP) in the Arabic-speaking community. Four multilingual translation...
View ArticleTop-$K$ ranking with a monotone adversary
In this paper, we address the top-$K$ ranking problem with a monotone adversary. We consider the scenario where a comparison graph is randomly generated and the adversary is allowed to add arbitrary...
View ArticleBenchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT
Retrieval pipelines-an integral component of many machine learning systems-perform poorly in domains where documents are long (e.g., 10K tokens or more) and where identifying the relevant document...
View ArticleLearning Optimal Tax Design in Nonatomic Congestion Games
We study how to learn the optimal tax design to maximize the efficiency in nonatomic congestion games. It is known that self-interested behavior among the players can damage the system's efficiency....
View ArticleAnalyzing Currency Fluctuations: A Comparative Study of GARCH, EWMA, and IV...
In this study, we examine the fluctuation in the value of the Great Britain Pound (GBP). We focus particularly on its relationship with the United States Dollar (USD) and the Euro (EUR) currency pairs....
View ArticleAn Empirical Study Into What Matters for Calibrating Vision-Language Models
Vision--Language Models (VLMs) have emerged as the dominant approach for zero-shot recognition, adept at handling diverse scenarios and significant distribution changes. However, their deployment in...
View ArticleA Closer Look at the Robustness of Contrastive Language-Image Pre-Training...
Contrastive Language-Image Pre-training (CLIP) models have demonstrated remarkable generalization capabilities across multiple challenging distribution shifts. However, there is still much to be...
View ArticleConformal Predictive Programming for Chance Constrained Optimization
Motivated by the advances in conformal prediction (CP), we propose conformal predictive programming (CPP), an approach to solve chance constrained optimization (CCO) problems, i.e., optimization...
View ArticleTeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI...
Electronic-photonic computing systems offer immense potential in energy-efficient artificial intelligence (AI) acceleration tasks due to the superior computing speed and efficiency of optics,...
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