ICML 2023 Topological Deep Learning Challenge : Design and Results
Mathilde Papillon
Mustafa Hajij
Helen Jenne
Johan Mathe
Audun Myers
Theodore Papamarkou
Tolga Birdal
Tamal K. Dey
Timothy Doster
Tegan H. Emerson
Gurusankar Gopalakrishnan
D. Govil
Aldo Guzmán-Sáenz
Henry Kvinge
Neal Livesay
Soham Mukherjee
Shreyas N. Samaga
K. Ramamurthy
Maneel Reddy Karri
Paul Rosen
Sophia Sanborn
Robin G. Walters
Jens Agerberg
Sadrodin Barikbin
Claudio Battiloro
Gleb Bazhenov
Guillermo Bernardez
Aiden Brent
Sergio Escalera
Simone Fiorellino
Dmitrii Gavrilev
Mohammed Hassanin
Paul Hausner
Odin Hoff Gardaa
Abdelwahed Khamis
M. Lecha
German Magai
Tatiana Malygina
Rubén Ballester
K. Nadimpalli
Alexander Nikitin
Abraham Rabinowitz
Alessandro Salatiello
Simone Scardapane
Luca Scofano
Suraj Singh
Jens Sjölund
Paul Snopov
Indro Spinelli
Lev Telyatnikov
Lucia Testa
Maosheng Yang
Yixiao Yue
Olga Zaghen
Ali Zia
Nina Miolane

Abstract
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topological neural networks from the literature by contributing to the python packages TopoNetX (data processing) and TopoModelX (deep learning). The challenge attracted twenty-eight qualifying submissions in its two-month duration. This paper describes the design of the challenge and summarizes its main findings.
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