3 papers
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…
cs.LG2025
Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures
Mathilde Papillon, Sophia Sanborn, Johan Mathe +8
The enduring legacy of Euclidean geometry underpins classical machine learning, which, for decades, has been primarily developed for data lying in Euclidean space. Yet, modern mach…