A Large Deviations Perspective on Policy Gradient Algorithms

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Reduced-order models (ROMs) are widely used in scientific computing to tackle high-dimensional systems. However, traditional ROM methods may only partially capture the intrinsic geometric characteristics of the data. These characteristics encompass the und ...

Approximate multipliers (AppMults) are widely employed in deep neural network (DNN) accelerators to reduce the area, delay, and power consumption. However, the inaccuracies of AppMults degrade DNN accuracy, necessitating a retraining process to recover acc ...

Lipschitz-constrained neural networks have several advantages over unconstrained ones and can be applied to a variety of problems, making them a topic of attention in the deep learning community. Unfortunately, it has been shown both theoretically and empi ...

In this paper, we consider the optimisation of a time varying scalar field by a network of agents with no gradient information. We propose a composite control law, blending extremum seeking with formation control in order to converge to the extrema faster ...

Finding optimal bidding strategies for generation units in electricity markets would result in higher profit. However, it is a challenging problem due to the system uncertainty which is due to the lack of knowledge of the strategies of other generation uni ...

In healthcare, data privacy of patients regulations prohibits data from being moved outside the hospital, preventing international medical datasets from being centralized for AI training. Federated learning (FL) is a data privacy-focused method that trains ...

Occluded person re-identification (ReID) is a person retrieval task which aims at matching occluded person images with holistic ones. For addressing occluded ReID, part-based methods have been shown beneficial as they offer fine-grained information and are ...

Text-to-image models, such as Stable Diffusion, can generate high-quality images from simple textual prompts. With methods such as Textual Inversion, it is possible to expand the vocabulary of these models with additional concepts, by learning the vocabula ...

The convergence speed of machine learning models trained with Federated Learning is significantly affected by non-independent and identically distributed (non-IID) data partitions, even more so in a fully decentralized setting without a central server. In ...

The growing adoption of point clouds as an imaging modality has stimulated the search for efficient solutions for compression. Learning-based algorithms have been reporting increasingly better performance and are drawing the attention from the research com ...

Simultaneous prediction of wrist and hand motions is essential for the natural interaction with hand prostheses. In this paper, we propose a novel multi-out Gaussian process (MOGP) model and a multi-task deep learning (MTDL) algorithm to achieve simultaneo ...

In wearable-based human activity recognition (HAR) research, one of the major challenges is the large intra-class variability problem. The collected activity signal is often, if not always, coupled with noises or bias caused by personal, environmental, or ...

This paper contributes to the intersection of operations research and machine learning in the context of autonomous ridesharing. In this work, autonomous ridesharing operations are reproduced through an event-based simulation approach and are modeled as a ...

Learning to detect entity mentions without using syntactic information can be useful for integration and joint optimization with other tasks. However, it is common to have partially annotated data for this problem. Here, we investigate two approaches to de ...

The need for running complex Machine Learning (ML) algorithms, such as Convolutional Neural Networks (CNNs), in edge devices, which are highly constrained in terms of computing power and energy, makes it important to execute such applications efficiently. ...

The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications. In this paper, we propose a non-parametric approximation of the likelihood ...

A Large Deviations Perspective on Policy Gradient Algorithms

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OPTIMAL TRANSPORT-INSPIRED DEEP LEARNING FRAMEWORK FOR SLOW-DECAYING KOLMOGOROV \bfitn-WIDTH PROBLEMS: EXPLOITING SINKHORN LOSS AND WASSERSTEIN KERNEL

Gradient Approximation of Approximate Multipliers for High-Accuracy Deep Neural Network Retraining

Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions

Gradient free cooperative seeking of a moving source

Multi-agent reinforcement learning with graph convolutional neural networks for optimal bidding strategies of generation units in electricity markets

Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems

Body Part-Based Representation Learning for Occluded Person Re-Identification

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning

Latent Space Slicing for Enhanced Entropy Modeling in Learning-Based Point Cloud Geometry Compression

Simultaneous Prediction of Wrist and Hand Motions via Wearable Ultrasound Sensing for Natural Control of Hand Prostheses

Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition

A machine learning-driven two-phase metaheuristic for autonomous ridesharing operations

Partially-supervised Mention Detection

A Product Engine for Energy-Efficient Execution of Binary Neural Networks Using Resistive Memories

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation