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

Adele  Myers; Sophia  Sanborn; Claire Donnat Donnat; Stefan Horst Sommer

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

Adele Myers, Sophia Sanborn, Claire Donnat Donnat, Stefan Horst Sommer

Image Analysis, Computational Modelling and Geometry

Publikation: Konferencebidrag › Paper › Forskning

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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.

Originalsprog	Engelsk
Publikationsdato	2022
Antal sider	8
Status	Udgivet - 2022
Begivenhed	ICLR 2022 Workshop on Geometrical and Topological Representation Learnings - ICLR 2022 - Virtual Varighed: 29 apr. 2022 → 29 apr. 2022

Workshop

Workshop	ICLR 2022 Workshop on Geometrical and Topological Representation Learnings - ICLR 2022
By	Virtual
Periode	29/04/2022 → 29/04/2022

Adgang til dokumentet

ICLR 2022 CHALLENGE FORForlagets udgivne version, 109 KBLicens: CC BY

Citationsformater

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title = "ICLR 2022 Challenge for Computational Geometry & Topology: Design and Results",

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.",

author = "Adele Myers and Sophia Sanborn and Donnat, {Claire Donnat} and Sommer, {Stefan Horst}",

year = "2022",

language = "English",

note = "ICLR 2022 Workshop on Geometrical and Topological Representation Learnings - ICLR 2022<br/> ; Conference date: 29-04-2022 Through 29-04-2022",

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T2 - ICLR 2022 Workshop on Geometrical and Topological Representation Learnings - ICLR 2022<br/>

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AU - Sanborn, Sophia

AU - Donnat, Claire Donnat

AU - Sommer, Stefan Horst

PY - 2022

Y1 - 2022

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AB - 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.

M3 - Paper

Y2 - 29 April 2022 through 29 April 2022

ER -