학술논문
ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results
Document Type
Working Paper
Author
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
Source
Subject
Language
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.