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Article Dans Une Revue Proceedings of Machine Learning Research Année : 2022

ICLR 2022 Challenge for Computational Geometry & Topology: Design and Results

Johan Mathe
  • Fonction : Auteur

Résumé

This paper presents the computational challenge on differential geometry and topology that was hosted within the ICLR 2022 workshop “Geometric and Topo- logical Representation Learning”. The competition asked participants to provide implementations of machine learning algorithms on manifolds that would respect the API of the open-source software Geomstats (manifold part) and Scikit-Learn (machine learning part) or PyTorch. The challenge attracted seven teams in its two month duration. This paper describes the design of the challenge and summarizes its main findings.
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Dates et versions

hal-03903044 , version 1 (16-12-2022)

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Adele Myers, Saiteja Utpala, Shubham Talbar, Sophia Sanborn, Christian Shewmake, et al.. ICLR 2022 Challenge for Computational Geometry & Topology: Design and Results. Proceedings of Machine Learning Research, 2022, Topological, Algebraic and Geometric Learning Workshops 2022, 196, pp.269-276. ⟨10.5281/zenodo.6554616⟩. ⟨hal-03903044⟩
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