7 citations · 7 across the 3 of their papers we have counts for
3 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…
RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds
Thibault de Surrel, Felix Hensel, Mathieu Carrière +5
The use of topological descriptors in modern machine learning applications, such as Persistence Diagrams (PDs) arising from Topological Data Analysis (TDA), has shown great potenti…