Concept

Torch (machine learning)

Publications associées (30)

Byzantine Machine Learning: A Primer

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot

The problem of Byzantine resilience in distributed machine learning, a.k.a., Byzantine machine learning, consists in designing distributed algorithms that can train an accurate model despite the presence of Byzantine nodes, i.e., nodes with corrupt data or ...

2023

Deep Learning Meets Sparse Regularization

Rahul Parhi

Deep learning (DL) has been wildly successful in practice, and most of the state-of-the-art machine learning methods are based on neural networks (NNs). Lacking, however, is a rigorous mathematical theory that adequately explains the amazing performance of ...

2023

Hyperdimensional computing for biosignal monitoring: Applications for epilepsy detection

Una Pale

Hyperdimensional (HD) computing is a novel approach to machine learning inspired by neuroscience, which uses vectors in a hyper-dimensional space to represent data and models. This approach has gained significant interest in recent years with applications ...

EPFL2023

Graph Regularization Network With Semantic Affinity For Weakly-Supervised Temporal Action Localization

Seungryong Kim, Jiyoung Lee

This paper presents a novel deep architecture for weakly-supervised temporal action localization that not only generates segment-level action responses but also propagates segment-level responses to the neighborhood in a form of graph Laplacian regularizat ...

IEEE2019

The effect of frontoparietal paired associative stimulation on decision-making and working memory

Traian Popa

Previous single-site neurostimulation experiments have unsuccessfully attempted to shift decision-making away from habitual control, a fast, inflexible cognitive strategy, towards goal-directed control, a flexible, though computationally expensive strategy ...

ELSEVIER MASSON, CORPORATION OFFICE2019

Performance limits of stochastic sub-gradient learning, Part I: Single agent case

Ali H. Sayed, Bicheng Ying

In this work and the supporting Part II [1], we examine the performance of stochastic sub-gradient learning strategies under weaker conditions than usually considered in the literature. The new conditions are shown to be automatically satisfied by several ...

Elsevier2018

Performance limits of stochastic sub-gradient learning, Part II: Multi-agent case

Ali H. Sayed, Bicheng Ying

The analysis in Part I [1] revealed interesting properties for subgradient learning algorithms in the context of stochastic optimization. These algorithms are used when the risk functions are non-smooth or involve non-differentiable components. They have b ...

Elsevier2018

Leveraging Convolutional Pose Machines for Fast and Accurate Head Pose Estimation

Jean-Marc Odobez, Olivier Canévet

We propose a head pose estimation framework that leverages on a recent keypoint detection model. More specifically, we apply the convolutional pose machines (CPMs) to input images, extract different types of facial keypoint features capturing appearance in ...

IEEE2018

Self-Aware Wearable Systems in Epileptic Seizure Detection

David Atienza Alonso, Amir Aminifar, Farnaz Forooghifar

Today, wearable systems are facing fundamental barriers in terms of battery lifetime and quality of their results. The main challenge in wearable systems is to increase the battery lifetime, while maintaining the machine-learning performance of the system. ...

2018

Transient networks of spatio-temporal connectivity map communication pathways in brain functional systems

Jean-Philippe Thiran, Pierre Vandergheynst, Alessandra Griffa, Kirell Maël Benzi, Patric Hagmann, Alessandro Daducci, Xavier Bresson, Benjamin Ricaud

The study of brain dynamics enables us to characterize the time-varying functional connectivity among distinct neural groups. However, current methods suffer from the absence of structural connectivity information. We propose to integrate infra-slow neural ...

Elsevier2017

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