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.
翻译:本文介绍了在2022年国际克拉里昂研究中心“地球测量和地形代表性学习”讲习班内主办的关于差异几何和地形学的计算挑战。竞争要求参与者在尊重开放源码软件“地理统计”(固定部分)和Scikit-Learn(机械学习部分)或PyTorch的API的多个方面实施机器学习算法。这一挑战在两个月内吸引了七个小组参加。本文描述了挑战的设计并概述了其主要结果。