ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results
Computational Geometry
2022-06-28 v2
Abstract
This paper presents the computational challenge on differential geometry and topology that was hosted within the ICLR 2022 workshop ``Geometric and Topological 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.
Keywords
Cite
@article{arxiv.2206.09048,
title = {ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results},
author = {Adele Myers and Saiteja Utpala and Shubham Talbar and Sophia Sanborn and Christian Shewmake and Claire Donnat and Johan Mathe and Umberto Lupo and Rishi Sonthalia and Xinyue Cui and Tom Szwagier and Arthur Pignet and Andri Bergsson and Soren Hauberg and Dmitriy Nielsen and Stefan Sommer and David Klindt and Erik Hermansen and Melvin Vaupel and Benjamin Dunn and Jeffrey Xiong and Noga Aharony and Itsik Pe'er and Felix Ambellan and Martin Hanik and Esfandiar Nava-Yazdani and Christoph von Tycowicz and Nina Miolane},
journal= {arXiv preprint arXiv:2206.09048},
year = {2022}
}