4 citations · 4 across the 15 of their papers we have counts for
18 papers
A new approach for estimating the number of spikes in high-dimensional Gaussian models
Hugo Jeannin, Pascal Vallet, Florent Bouchard +2
In this paper, we study the estimation of the signal subspace dimension in a -variate signal-plus-noise complex Gaussian model, when the noise covariance matrix is assumed t…
Personalised federated learning for Riemannian and Euclidean EEG decoding
Thibault Pautrel, Florent Bouchard, Ammar Mian +1
Federated learning (FL) lets EEG decoders learn from recordings of several subjects without pooling them. We consider two light EEG decoders, the Riemannian SPDNet and the Euclidea…
Benchmarking External Generalization of SPD Matrix Learning for Resting-State fMRI Connectome Prediction
Ce Ju, Antoine Collas, Florent Bouchard +1
Resting-state functional magnetic resonance imaging (rs-fMRI) functional connectivity (FC) matrices are widely used for individual-level prediction, but strong performance within o…
FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet
Thibault Pautrel, Florent Bouchard, Ammar Mian +1
We introduce two federated learning frameworks for the classical SPDnet model operating on symmetric positive definite (SPD) matrices with Stiefel-constrained parameters. Unlike st…
SPD Learn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization
Bruno Aristimunha, Ce Ju, Antoine Collas +5
Implementations of symmetric positive definite (SPD) matrix-based neural networks for neural decoding remain fragmented across research codebases and Python packages. Existing impl…
Leveraging Low-rank Factorizations of Conditional Correlation Matrices in Graph Learning
Thu Ha Phi, Alexandre Hippert-Ferrer, Florent Bouchard +1
This paper addresses the problem of learning an undirected graph from data gathered at each nodes. Within the graph signal processing framework, the topology of such graph can be l…