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Publications associées (195)

Theory of Deep Learning: Neural Tangent Kernel and Beyond

In the recent years, Deep Neural Networks (DNNs) have managed to succeed at tasks that previously appeared impossible, such as human-level object recognition, text synthesis, translation, playing games and many more. In spite of these major achievements, o ...

EPFL2022

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Chang-Hyun Park

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology unde ...

OXFORD UNIV PRESS INC2021

Propagation on Multi-relational Graphs for Node Regression

Eda Bayram

Recent years have witnessed a rise in real-world data captured with rich structural information that can be conveniently depicted by multi-relational graphs. While inference of continuous node features across a simple graph is rather under-studied by the c ...

2021

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

Pedestrians are arguably one of the most safety-critical road users to consider for autonomous vehicles in urban areas. In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes from a single image. The ...

2021

Mixed Nash Equilibria in the Adversarial Examples Game

Rafaël Benjamin Pinot

This paper tackles the problem of adversarial examples from a game theoretic point of view. We study the open question of the existence of mixed Nash equilibria in the zero-sum game formed by the attacker and the classifier. While previous works usually al ...

2021

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

Fernando Perez Cruz

The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss ...

2021

Counting using deep learning regression gives value to ecological surveys

Devis Tuia, Benjamin Alexander Kellenberger

Many ecological studies rely on count data and involve manual counting of objects of interest, which is time-consuming and especially disadvantageous when time in the field or lab is limited. However, an increasing number of works uses digital imagery, whi ...

2021

Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent

Rachid Guerraoui, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault

Byzantine-resilient Stochastic Gradient Descent (SGD) aims at shielding model training from Byzantine faults, be they ill-labeled training datapoints, exploited software/hardware vulnerabilities, or malicious worker nodes in a distributed setting. Two rece ...

2021

Residential density classification for sustainable housing development using a machine learning approach

Dan Assouline, Nahid Mohajeri Pour Rayeni, Alina Ursula Rosa Walch

Using Machine Learning (ML) algorithms for classification of the existing residential neighbourhoods and their spatial characteristics (e.g. density) so as to provide plausible scenarios for designing future sustainable housing is a novel application. Here ...

IOP PUBLISHING LTD2021

Injecting Knowledge in Data-driven Vehicle Trajectory Predictors

Alexandre Massoud Alahi, Ismail Nejjar, Mohammadhossein Bahari

Vehicle trajectory prediction tasks have been commonly tackled from two distinct perspectives: either with knowledge-driven methods or more recently with data-driven ones. On the one hand, we can explicitly implement domain-knowledge or physical priors suc ...

2021

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