ICML 2023 Topological Deep Learning Challenge : Design and Results

• DOI: 10.48550/arxiv.2309.15188
• Publication Date: 2023-01-01
• Authors: Mathilde Papillon; Mustafa Hajij; Florian Frantzen; Josef Hoppe; Helen Jenne; Johan Mathe; Audun Myers; Theodore Papamarkou; Michael T. Schaub; Ghada Zamzmi; Tolga Birdal; Tamal K. Dey; Tim Doster; Tegan Emerson; Gurusankar Gopala...; Devendra Govil; Vincent P. Grande; Aldo Guzmán-Sáenz; Henry Kvinge; Neal Livesay; Jan Meisner; Soham Mukherjee; Shreyas N. Samaga; Karthikeyan Nates...; Maneel Reddy Karri; Paul Rosen; Sophia Sanborn; Michael Scholkemper; Robin Walters; Jens Agerberg; Georg Bökman; Sadrodin Barikbin; Claudio Battiloro; Gleb Bazhenov; Guillermo Bernárdez; Aiden Brent; Sérgio Escalera; Simone Fiorellino; Dmitrii Gavrilev; Mohammed Hassanin; Paul Häusner; Odin Hoff Gardaa; Abdelwahed Khamis; Manuel Lecha; German Magai; Tatiana Malygina; Pavlo Melnyk; Rubén Ballester; Kalyan Varma Nadi...; Alexander Nikitin; Abraham Rabinowitz; Alessandro Salati...; 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
• Keywords: Python; Implementation

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