ICLR 2022 Challenge for Computational Geometry & Topology: Design and Results - Archive ouverte HAL
Journal Articles Proceedings of Machine Learning Research Year : 2022

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

Christian Shewmake
Claire Donnat
Johan Mathe
  • Function : Author
Nina Miolane
  • Function : Author
  • PersonId : 951696
  • IdRef : 199540519

Abstract

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.
Fichier principal
Vignette du fichier
myers22a (1).pdf (128.27 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

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

Identifiers

Cite

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⟩
118 View
139 Download

Altmetric

Share

More