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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Veröffentlicht in: | arXiv.org 2022-06 |
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Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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. |
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ISSN: | 2331-8422 |
DOI: | 10.48550/arxiv.2206.09048 |