Myers, A, Utpala, S, Talbar, S, Sanborn, S, Shewmake, C, Donnat, C, Mathe, J, Lupo, U, Sonthalia, R, Cui, X, Szwagier, T, Pignet, A, Bergsson, A, Hauberg, S, Nielsen, D, Sommer, S, Klindt, D, Hermansen, E, Vaupel, M, Dunn, B, Xiong, J, Aharony, N, Pe'er, I, Ambellan, F, Hanik, M, Nava-Yazdani, E, von Tycowicz, C, Miolane, N (January 2022) ICLR 2022 CHALLENGE FOR COMPUTATIONAL GEOMETRY & TOPOLOGY: DESIGN AND RESULTS. In: UNSPECIFIED.
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| CSHL Authors: | |
| Communities: | CSHL labs > Klindt lab |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | 1 January 2022 |
| Date Deposited: | 11 Apr 2024 15:15 |
| Last Modified: | 11 Apr 2024 15:15 |
| URI: | https://repository.cshl.edu/id/eprint/41492 |
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