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arxiv: 2309.15188 · v4 · pith:2UKTP546 · submitted 2023-09-26 · cs.LG

ICML 2023 Topological Deep Learning Challenge : Design and Results

Mathilde Papillon , Mustafa Hajij , Helen Jenne , Johan Mathe , Audun Myers , Theodore Papamarkou , Tolga Birdal , Tamal Dey
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Tim Doster Tegan Emerson Gurusankar Gopalakrishnan Devendra Govil Aldo Guzm\'an-S\'aenz Henry Kvinge Neal Livesay Soham Mukherjee Shreyas N. Samaga Karthikeyan Natesan Ramamurthy Maneel Reddy Karri Paul Rosen Sophia Sanborn Robin Walters Jens Agerberg Sadrodin Barikbin Claudio Battiloro Gleb Bazhenov Guillermo Bernardez Aiden Brent Sergio Escalera Simone Fiorellino Dmitrii Gavrilev Mohammed Hassanin Paul H\"ausner Odin Hoff Gardaa Abdelwahed Khamis Manuel Lecha German Magai Tatiana Malygina Rub\'en Ballester Kalyan Nadimpalli Alexander Nikitin Abraham Rabinowitz Alessandro Salatiello Simone Scardapane Luca Scofano Suraj Singh Jens Sj\"olund Pavel Snopov Indro Spinelli Lev Telyatnikov Lucia Testa Maosheng Yang Yixiao Yue Olga Zaghen Ali Zia Nina Miolane
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classification cs.LG
keywords challengelearningdeeptopologicaldesignicmlaskedattracted
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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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