Michaël Defferrard

This person is no longer with EPFL

About
Privacy
Disclaimer

Graph Chatbot

Related publications (5)

RosettaSurf-A surface-centric computational design approach

Pierre Vandergheynst, Michaël Defferrard, Bruno Emanuel Ferreira De Sousa Correia, Jaume Bonet Martinez, Andreas Loukas, Andreas Scheck, Stéphane Rosset

Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted wi ...

PUBLIC LIBRARY SCIENCE2022

Leveraging topology, geometry, and symmetries for efficient Machine Learning

Michaël Defferrard

When learning from data, leveraging the symmetries of the domain the data lies on is a principled way to combat the curse of dimensionality: it constrains the set of functions to learn from. It is more data efficient than augmentation and gives a generaliz ...

EPFL2022

Connectome spectral analysis to track EEG task dynamics on a subsecond scale

Patric Hagmann, Sébastien Tourbier, Gijs Plomp, Michaël Defferrard, David Pascucci

We present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in order ...

2020

DeepSphere: Efficient spherical convolutional neural network with HEALPix sampling for cosmological applications

Nathanaël Perraudin, Michaël Defferrard

Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning toolbox and have led to many breakthroughs in Artificial Intelligence. So far, these neural networks (NNs) have mostly been developed for regular Euclidean domains such as those su ...

ELSEVIER SCIENCE BV2019

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Pierre Vandergheynst, Xavier Bresson, Michaël Defferrard

In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or wor ...

2016

Page 1 of 1