5 citations · 5 across the 4 of their papers we have counts for
4 papers
A probabilistic view on Riemannian machine learning models for SPD matrices
Thibault de Surrel, Florian Yger, Fabien Lotte +1
The goal of this paper is to show how different machine learning tools on the Riemannian manifold of Symmetric Positive Definite (SPD) matrices can be united under…
Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices
Thibault de Surrel, Fabien Lotte, Sylvain Chevallier +1
Circular and non-flat data distributions are prevalent across diverse domains of data science, yet their specific geometric structures often remain underutilized in machine learnin…
Geodesic Optimization for Predictive Shift Adaptation on EEG data
Apolline Mellot, Antoine Collas, Sylvain Chevallier +2
Electroencephalography (EEG) data is often collected from diverse contexts involving different populations and EEG devices. This variability can induce distribution shifts in the d…
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
Apolline Mellot, Antoine Collas, Sylvain Chevallier +2
Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability. ML algorithms typically require i…