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ICML 2023 Topological Deep Learning Challenge : Design and Results

Fuente: arXiv

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Bibliographische Detailangaben
Hauptverfasser:	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., Ramamurthy, Karthikeyan Natesan, Karri, Maneel Reddy, 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, Gardaa, Odin Hoff, Khamis, Abdelwahed, 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
Format:	Preprint
Veröffentlicht:	2023
Schlagworte:	Machine Learning
Online-Zugang:	Acceder al recurso
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_version_	1866929213840293888
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. Ramamurthy, Karthikeyan Natesan Karri, Maneel Reddy 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 Gardaa, Odin Hoff Khamis, Abdelwahed 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
author_facet	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. Ramamurthy, Karthikeyan Natesan Karri, Maneel Reddy 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 Gardaa, Odin Hoff Khamis, Abdelwahed 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
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2309_15188
institution	arXiv
publishDate	2023
record_format	arxiv
spellingShingle	ICML 2023 Topological Deep Learning Challenge : Design and Results 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. Ramamurthy, Karthikeyan Natesan Karri, Maneel Reddy 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 Gardaa, Odin Hoff Khamis, Abdelwahed 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 Machine Learning 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.
title	ICML 2023 Topological Deep Learning Challenge : Design and Results
topic	Machine Learning
url	https://arxiv.org/abs/2309.15188