activity
20202026
most citedGeomstats: A Python Package for Riemannian Geometry in Machine Learning

96 citations · 107 across the 6 of their papers we have counts for

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2 papers · 1 filter

cs.LG2026

OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data

Louisa Cornelis, Johan Mathe, Louis Van Langendonck +2

Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning. Yet most benchmarks focus on the regime , where the number of graphs $n…

cs.LG2026

bispectrum: Selective -Bispectra Made Practical

Johan Mathe, Adele Myers, Simon Mataigne +1

Many machine learning tasks are invariant under the action of a group of transformations: signal classification can be invariant under translations, image classification under…