Publications associées à Empirical risk minimization

On The Choice of Graph Neural Network Architectures

Pascal Frossard, Guillermo Ortiz Jimenez, Clément Arthur Yvon Vignac

Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals. With the development of graph networks, this setup has become a de facto benchmark for a sign ...

2020

Dynamic Federated Learning

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. While many federated learning architectures process data in an online manner, and are hence adaptive by nature, most performance analyse ...

IEEE2020

Comparison of crack segmentation using digital image correlation measurements and deep learning

Katrin Beyer, Radhakrishna Achanta, Amir Rezaie, Michele Godio

Reliable methods for detecting pixels that represent cracks from laboratory images taken for digital image correlation (DIC) are required for two main reasons. Firstly, the segmented crack maps are used as an input for some DIC methods that are based on di ...

2020

Hinges for origami-inspired structures by multimaterial additive manufacturing

Jamie Paik, Jian-Lin Huang, Ralph Spolenak

Recent studies demonstrate novel metamaterials featuring unique properties by combining origami-inspired designs with additive manufacturing. In particular, the use of flexural hinges endow distinct advantages toward miniaturization and fabrication; howeve ...

2020

Stochastic Learning Under Random Reshuffling With Constant Step-Sizes

Ali H. Sayed, Stefan Vlaski, Bicheng Ying, Kun Yuan

In empirical risk optimization, it has been observed that stochastic gradient implementations that rely on random reshuffling of the data achieve better performance than implementations that rely on sampling the data uniformly. Recent works have pursued ju ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Regularization via Mass Transportation

Daniel Kuhn, Soroosh Shafieezadeh Abadeh, Peyman Mohajerin Esfahani

The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution. When facing scarce training data, ...

2019

Supervised Learning Under Distributed Features

Ali H. Sayed, Bicheng Ying, Kun Yuan

This paper studies the problem of learning under both large datasets and large-dimensional feature space scenarios. The feature information is assumed to be spread across agents in a network, where each agent observes some of the features. Through local co ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Remote Blood Pulse Analysis for Face Presentation Attack Detection

Sébastien Marcel, Guillaume Heusch

In this chapter, the usage of Remote Photoplethysmography (rPPG\index{Remote Photoplethysmography (rPPG)}) as a mean for face presentation attack detection is investigated. Remote photoplethysmography consists in retrieving the heart-rate of a subject from ...

Springer2019

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Empirical risk minimization

On The Choice of Graph Neural Network Architectures

Dynamic Federated Learning

Comparison of crack segmentation using digital image correlation measurements and deep learning

Hinges for origami-inspired structures by multimaterial additive manufacturing

Stochastic Learning Under Random Reshuffling With Constant Step-Sizes

Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator

An accelerated communication-efficient primal-dual optimization framework for structured machine learning

Regularization via Mass Transportation

Supervised Learning Under Distributed Features

Remote Blood Pulse Analysis for Face Presentation Attack Detection

Graph Chatbot

Chattez avec Graph Search

Hinges for origami-inspired structures by multimaterial additive manufacturing

Remote Blood Pulse Analysis for Face Presentation Attack Detection

Dynamic Federated Learning

Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator

Comparison of crack segmentation using digital image correlation measurements and deep learning

An accelerated communication-efficient primal-dual optimization framework for structured machine learning

On The Choice of Graph Neural Network Architectures

Stochastic Learning Under Random Reshuffling With Constant Step-Sizes

Regularization via Mass Transportation

Supervised Learning Under Distributed Features