ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results
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 tha...
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| creator | Myers, Adele Saiteja Utpala Talbar, Shubham Sanborn, Sophia Shewmake, Christian Donnat, Claire Mathe, Johan Lupo, Umberto Sonthalia, Rishi Cui, Xinyue Szwagier, Tom Pignet, Arthur Bergsson, Andri Hauberg, Soren Nielsen, Dmitriy Sommer, Stefan Klindt, David Hermansen, Erik Vaupel, Melvin Dunn, Benjamin Xiong, Jeffrey Aharony, Noga Pe'er, Itsik Ambellan, Felix Hanik, Martin Nava-Yazdani, Esfandiar Christoph von Tycowicz Miolane, Nina |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2206.09048 |
| format | Article |
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| subjects | Algorithms Computational geometry Computer Science - Computational Geometry Differential geometry Machine learning Manifolds (mathematics) Topology |
| title | ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results |
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