activity
20192021
most citedGeomstats: A Python Package for Riemannian Geometry in Machine Learning

96 citations · 104 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CG2021

ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results

Nina Miolane, Matteo Caorsi, Umberto Lupo +30

This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning…

math.DG2021

Parallel Transport on Kendall Shape Spaces

Nicolas Guigui, Elodie Maignant, Alain Trouvé +1

Kendall shape spaces are a widely used framework for the statistical analysis of shape data arising from many domains, often requiring the parallel transport as a tool to normalise…

math.DG2021★ 5 cited

A reduced parallel transport equation on Lie Groups with a left-invariant metric

Nicolas Guigui, Xavier Pennec

This paper presents a derivation of the parallel transport equation expressed in the Lie algebra of a Lie group endowed with a left-invariant metric.The use of this equation is exe…

cs.CV2021

Cardiac Motion Modeling with Parallel Transport and Shape Splines

Nicolas Guigui, Pamela Moceri, Maxime Sermesant +1

In cases of pressure or volume overload, probing cardiac function may be difficult because of the interactions between shape and deformations.In this work, we use the LDDMM framewo…

math.DG2020

Numerical Accuracy of Ladder Schemes for Parallel Transport on Manifolds

Nicolas Guigui, Xavier Pennec

Parallel transport is a fundamental tool to perform statistics on Rie-mannian manifolds. Since closed formulae don't exist in general, practitioners often have to resort to numeric…

stat.ML2020★ 3 cited

Classifying histograms of medical data using information geometry of beta distributions

Alice Le Brigant, Nicolas Guigui, Sana Rebbah +1

In this paper, we use tools of information geometry to compare, average and classify histograms. Beta distributions are fitted to the histograms and the corresponding Fisher inform…