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ICML 2023 Topological Deep Learning Challenge : Design and Results

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Vydáno v:arXiv.org (Jan 18, 2024), p. n/a
Hlavní autor: Papillon, Mathilde
Další autoři: 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
Vydáno:
Cornell University Library, arXiv.org
Témata:
Data processing
Neural networks
Machine learning
Deep learning
Topology
On-line přístup:Citation/Abstract
Full text outside of ProQuest
Tagy: Přidat tag
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MARC

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022 |a 2331-8422 
024 7 |a 10.5281/zenodo.7958513  |2 doi 
035 |a 2869800937 
045 0 |b d20240118 
100 1 |a Papillon, Mathilde 
245 1 |a ICML 2023 Topological Deep Learning Challenge : Design and Results 
260 |b Cornell University Library, arXiv.org  |c Jan 18, 2024 
513 |a Working Paper 
520 3 |a 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. 
653 |a Data processing 
653 |a Neural networks 
653 |a Machine learning 
653 |a Deep learning 
653 |a Topology 
700 1 |a Hajij, Mustafa 
700 1 |a Jenne, Helen 
700 1 |a Mathe, Johan 
700 1 |a Myers, Audun 
700 1 |a Papamarkou, Theodore 
700 1 |a Birdal, Tolga 
700 1 |a Dey, Tamal 
700 1 |a Doster, Tim 
700 1 |a Emerson, Tegan 
700 1 |a Gopalakrishnan, Gurusankar 
700 1 |a Govil, Devendra 
700 1 |a Guzmán-Sáenz, Aldo 
700 1 |a Kvinge, Henry 
700 1 |a Livesay, Neal 
700 1 |a Mukherjee, Soham 
700 1 |a Samaga, Shreyas N 
700 1 |a Karthikeyan Natesan Ramamurthy 
700 1 |a Maneel Reddy Karri 
700 1 |a Rosen, Paul 
700 1 |a Sanborn, Sophia 
700 1 |a Walters, Robin 
700 1 |a Agerberg, Jens 
700 1 |a Barikbin, Sadrodin 
700 1 |a Battiloro, Claudio 
700 1 |a Bazhenov, Gleb 
700 1 |a Bernardez, Guillermo 
700 1 |a Brent, Aiden 
700 1 |a Escalera, Sergio 
700 1 |a Fiorellino, Simone 
700 1 |a Gavrilev, Dmitrii 
700 1 |a Hassanin, Mohammed 
700 1 |a Häusner, Paul 
700 1 |a Odin Hoff Gardaa 
700 1 |a Abdelwahed Khamis 
700 1 |a Lecha, Manuel 
700 1 |a Magai, German 
700 1 |a Malygina, Tatiana 
700 1 |a Ballester, Rubén 
700 1 |a Nadimpalli, Kalyan 
700 1 |a Nikitin, Alexander 
700 1 |a Rabinowitz, Abraham 
700 1 |a Salatiello, Alessandro 
700 1 |a Scardapane, Simone 
700 1 |a Scofano, Luca 
700 1 |a Singh, Suraj 
700 1 |a Sjölund, Jens 
700 1 |a Snopov, Pavel 
700 1 |a Spinelli, Indro 
700 1 |a Telyatnikov, Lev 
700 1 |a Testa, Lucia 
700 1 |a Yang, Maosheng 
700 1 |a Yue, Yixiao 
700 1 |a Zaghen, Olga 
700 1 |a Zia, Ali 
700 1 |a Miolane, Nina 
773 0 |t arXiv.org  |g (Jan 18, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2869800937/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2309.15188