Publications de Michaël Defferrard

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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 ...

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 ...

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 ...

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 ...

Michaël Defferrard

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Leveraging topology, geometry, and symmetries for efficient Machine Learning

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

RosettaSurf-A surface-centric computational design approach

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering