35 citations · 143 across the 27 of their papers we have counts for
27 papers
The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark
Sylvain Chevallier, Igor Carrara, Bruno Aristimunha +6
Objective. This study conduct an extensive Brain-computer interfaces (BCI) reproducibility analysis on open electroencephalography datasets, aiming to assess existing solutions and…
PAVI: Plate-Amortized Variational Inference
Louis Rouillard, Alexandre Le Bris, Thomas Moreau +1
Given observed data and a probabilistic generative model, Bayesian inference searches for the distribution of the model's parameters that could have yielded the data. Inference is…
Test like you Train in Implicit Deep Learning
Zaccharie Ramzi, Pierre Ablin, Gabriel Peyré +1
Implicit deep learning has recently gained popularity with applications ranging from meta-learning to Deep Equilibrium Networks (DEQs). In its general formulation, it relies on exp…
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy +4
When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals. Learning with…
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
Mathieu Dagréou, Thomas Moreau, Samuel Vaiter +1
Bilevel optimization problems, which are problems where two optimization problems are nested, have more and more applications in machine learning. In many practical cases, the uppe…
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels
Guillaume Staerman, Cédric Allain, Alexandre Gramfort +1
Temporal point processes (TPP) are a natural tool for modeling event-based data. Among all TPP models, Hawkes processes have proven to be the most widely used, mainly due to their…