4 citations · 10 across the 15 of their papers we have counts for
3 papers · 1 filter
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…
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…