ICML 2023 Topological Deep Learning Challenge
Design and Results
Mathilde Papillon (Challenge Organizer)
Mustafa Hajij (Challenge Reviewer, Challenge Organizer)
Audun Myers (Challenge Reviewer)
Florian Frantzen (Challenge Reviewer)
Ghada Zamzmi (Challenge Reviewer)
Helen Jenne (Challenge Reviewer)
Johan Mathe (Challenge Reviewer)
Josef Hoppe (Challenge Reviewer)
Maosheng Yang (TU Delft - Electrical Engineering, Mathematics and Computer Science)
undefined More Authors (External organisation)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.