ICML 2023 Topological Deep Learning Challenge: Design and Results

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.

 @InProceedings{pmlr-v221-papillon23a,
title = 	 {ICML 2023 Topological Deep Learning Challenge: Design and Results},
author =       {Papillon, Mathilde and Hajij, Mustafa and Myers, Audun and Frantzen, Florianand and Zamzmi, Ghada and Jenne, Helen and Mathe, Johan and Hoppe, Josef and Schaub, Michael and Papamarkou, Theodore and Guzmán-Sáenz, Aldo and Rieck, Bastian and Livesay, Neal and Dey, Tamal and Rabinowitz, Abraham and Brent, Aiden and Salatiello, Alessandro and Nikitin, Alexander and Zia, Ali and Battiloro, Claudio and Gavrilev, Dmitrii and Bökman, Georg and Magai, German and Bazhenov, Gleb and Bernardez, Guillermo and Spinelli, Indro and Agerberg, Jens and Nadimpalli, Kalyan and Telyatninkov, Lev and Scofano, Luca and Testa, Lucia and Lecha, Manuel and Yang, Maosheng and Hassanin, Mohammed and Gardaa, Odin Hoff and Zaghen, Olga and Hausner, Paul and Snopoff, Paul and Melnyk, Pavlo and Ballester, Rubén and Barikbin, Sadrodin and Escalera, Sergio and Fiorellino, Simone and Kvinge, Henry and Meissner, Jan and Ramamurthy, Karthikeyan Natesan and Scholkemper, Michael and Rosen, Paul and Walters, Robin and Samaga, Shreyas N. and Mukherjee, Soham and Sanborn, Sophia and Emerson, Tegan and Doster, Timothy and Birdal, Tolga and Grande, Vincent and Khamis, Abdelwahed and Scardapane, Simone and Singh, Suraj and Malygina, Tatiana and Yue, Yixiao and Miolane, Nina},
booktitle = 	 {Proceedings of 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML)},
pages = 	 {3--8},
year = 	 {2023},
editor = 	 {Doster, Timothy and Emerson, Tegan and Kvinge, Henry and Miolane, Nina and Papillon, Mathilde and Rieck, Bastian and Sanborn, Sophia},
volume = 	 {221},
series = 	 {Proceedings of Machine Learning Research},
month = 	 {28 Jul},
publisher =    {PMLR},
pdf = 	 {https://proceedings.mlr.press/v221/papillon23a/papillon23a.pdf},
url = 	 {https://proceedings.mlr.press/v221/papillon23a.html},
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.} }