1 citations · 1 across the 5 of their papers we have counts for
5 papers
PSDNorm: Test-Time Temporal Normalization for Deep Learning in Sleep Staging
Théo Gnassounou, Antoine Collas, Rémi Flamary +1
Distribution shift poses a significant challenge in machine learning, particularly in biomedical applications using data collected across different subjects, institutions, and reco…
Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment
Théo Gnassounou, Antoine Collas, Rémi Flamary +2
Machine learning applications on signals such as computer vision or biomedical data often face significant challenges due to the variability that exists across hardware devices or…
SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities
Yanis Lalou, Théo Gnassounou, Antoine Collas +6
Unsupervised Domain Adaptation (DA) consists of adapting a model trained on a labeled source domain to perform well on an unlabeled target domain with some data distribution shift.…
Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications
Antoine Collas, Rémi Flamary, Alexandre Gramfort
This paper introduces a novel domain adaptation technique for time series data, called Mixing model Stiefel Adaptation (MSA), specifically addressing the challenge of limited label…
Convolutional Monge Mapping Normalization for learning on sleep data
Théo Gnassounou, Rémi Flamary, Alexandre Gramfort
In many machine learning applications on signals and biomedical data, especially electroencephalogram (EEG), one major challenge is the variability of the data across subjects, ses…