ICLR 2022挑战计算几何和拓扑：设计和结果

论文标题

ICLR 2022挑战计算几何和拓扑：设计和结果

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

论文作者

Myers, Adele, Utpala, Saiteja, 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, von Tycowicz, Christoph, Miolane, Nina

论文摘要

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两个月的时间。本文描述了挑战的设计，并总结了其主要发现。

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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