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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.

Details

1009240
Subject
Data processing;
Neural networks;
Machine learning;
Deep learning;
Topology
Identifier / keyword
Machine Learning
URL
http://arxiv.org/abs/2309.15188
Title
ICML 2023 Topological Deep Learning Challenge : Design and Results
Author
Papillon, Mathilde; Hajij, Mustafa; Jenne, Helen; Mathe, Johan; Myers, Audun; Papamarkou, Theodore; Birdal, Tolga; Dey, Tamal; Doster, Tim; Emerson, Tegan; Gopalakrishnan, Gurusankar; Govil, Devendra; Guzmán-Sáenz, Aldo; Kvinge, Henry; Livesay, Neal; Mukherjee, Soham; Samaga, Shreyas N; Karthikeyan Natesan Ramamurthy; Maneel Reddy Karri; Rosen, Paul; Sanborn, Sophia; Walters, Robin; Agerberg, Jens; Barikbin, Sadrodin; Battiloro, Claudio; Bazhenov, Gleb; Bernardez, Guillermo; Brent, Aiden; Escalera, Sergio; Fiorellino, Simone; Gavrilev, Dmitrii; Hassanin, Mohammed; Häusner, Paul; Odin Hoff Gardaa; Abdelwahed Khamis; Lecha, Manuel; Magai, German; Malygina, Tatiana; Ballester, Rubén; Nadimpalli, Kalyan; Nikitin, Alexander; Rabinowitz, Abraham; Salatiello, Alessandro; Scardapane, Simone; Scofano, Luca; Singh, Suraj; Sjölund, Jens; Snopov, Pavel; Spinelli, Indro; Telyatnikov, Lev; Testa, Lucia; Yang, Maosheng; Yue, Yixiao; Zaghen, Olga; Zia, Ali; Miolane, Nina
Publication title
arXiv.org; Ithaca
Publication year
2024
Publication date
Jan 18, 2024
Section
Computer Science
Publisher
Cornell University Library, arXiv.org
Source
arXiv.org
Place of publication
Ithaca
Country of publication
United States
University/institution
Cornell University Library arXiv.org
Publication subject
Biology, Statistics, Physics, Mathematics, Engineering--Electrical Engineering, Computers--Computer Engineering, Business And Economics--Banking And Finance
e-ISSN
2331-8422
Source type
Working Paper
Language of publication
English
Document type
Working Paper
Publication history
 
 
Online publication date
2024-01-19
Milestone dates
2023-09-26 (Submission v1); 2023-10-02 (Submission v2); 2023-12-19 (Submission v3); 2024-01-18 (Submission v4)
Publication history
 
 
   First posting date
19 Jan 2024
DOI
https://doi.org/10.5281/zenodo.7958513
ProQuest document ID
2869800937
Document URL
https://www.proquest.com/working-papers/icml-2023-topological-deep-learning-challenge/docview/2869800937/se-2?accountid=208611
Full text outside of ProQuest
http://arxiv.org/abs/2309.15188
Copyright
© 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2024-01-20
Database
ProQuest One Academic