Papillon, M., Hajij, M., Myers, A., Jenne, H., Mathe, J., Papamarkou, T., ... & Miolane, N.
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.
Citation
Papillon, M., Hajij, M., Myers, A., Jenne, H., Mathe, J., Papamarkou, T., ... & Miolane, N. (2023, September). Icml 2023 topological deep learning challenge: Design and results. In Topological, Algebraic and Geometric Learning Workshops 2023 (pp. 3-8). PMLR.
BibTeX
@InProceedings{pmlr-v221-papillon23a, title = {ICML 2023 Topological Deep Learning Challenge: Design and Results}, author = {Papillon, M., Miolane, N., et al.}, 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}, }
