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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.05211 |
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| _version_ | 1866929491877560320 |
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| author | Bernárdez, Guillermo Telyatnikov, Lev Montagna, Marco Baccini, Federica Papillon, Mathilde Ferriol-Galmés, Miquel Hajij, Mustafa Papamarkou, Theodore Bucarelli, Maria Sofia Zaghen, Olga Mathe, Johan Myers, Audun Mahan, Scott Lillemark, Hansen Vadgama, Sharvaree Bekkers, Erik Doster, Tim Emerson, Tegan Kvinge, Henry Agate, Katrina Ahmed, Nesreen K Bai, Pengfei Banf, Michael Battiloro, Claudio Beketov, Maxim Bogdan, Paul Carrasco, Martin Cavallo, Andrea Choi, Yun Young Dasoulas, George Elphick, Matouš Escalona, Giordan Filipiak, Dominik Fritze, Halley Gebhart, Thomas Gil-Sorribes, Manel Goomanee, Salvish Guallar, Victor Imasheva, Liliya Irimia, Andrei Jin, Hongwei Johnson, Graham Kanakaris, Nikos Koloski, Boshko Kovač, Veljko Lecha, Manuel Lee, Minho Leroy, Pierrick Long, Theodore Magai, German Martinez, Alvaro Masden, Marissa Mežnar, Sebastian Miquel-Oliver, Bertran Molina, Alexis Nikitin, Alexander Nurisso, Marco Piekenbrock, Matt Qin, Yu Rygiel, Patryk Salatiello, Alessandro Schattauer, Max Snopov, Pavel Suk, Julian Sánchez, Valentina Tec, Mauricio Vaccarino, Francesco Verhellen, Jonas Wantiez, Frederic Weers, Alexander Zajec, Patrik Škrlj, Blaž Miolane, Nina |
| author_facet | Bernárdez, Guillermo Telyatnikov, Lev Montagna, Marco Baccini, Federica Papillon, Mathilde Ferriol-Galmés, Miquel Hajij, Mustafa Papamarkou, Theodore Bucarelli, Maria Sofia Zaghen, Olga Mathe, Johan Myers, Audun Mahan, Scott Lillemark, Hansen Vadgama, Sharvaree Bekkers, Erik Doster, Tim Emerson, Tegan Kvinge, Henry Agate, Katrina Ahmed, Nesreen K Bai, Pengfei Banf, Michael Battiloro, Claudio Beketov, Maxim Bogdan, Paul Carrasco, Martin Cavallo, Andrea Choi, Yun Young Dasoulas, George Elphick, Matouš Escalona, Giordan Filipiak, Dominik Fritze, Halley Gebhart, Thomas Gil-Sorribes, Manel Goomanee, Salvish Guallar, Victor Imasheva, Liliya Irimia, Andrei Jin, Hongwei Johnson, Graham Kanakaris, Nikos Koloski, Boshko Kovač, Veljko Lecha, Manuel Lee, Minho Leroy, Pierrick Long, Theodore Magai, German Martinez, Alvaro Masden, Marissa Mežnar, Sebastian Miquel-Oliver, Bertran Molina, Alexis Nikitin, Alexander Nurisso, Marco Piekenbrock, Matt Qin, Yu Rygiel, Patryk Salatiello, Alessandro Schattauer, Max Snopov, Pavel Suk, Julian Sánchez, Valentina Tec, Mauricio Vaccarino, Francesco Verhellen, Jonas Wantiez, Frederic Weers, Alexander Zajec, Patrik Škrlj, Blaž Miolane, Nina |
| contents | This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem of representing data in different discrete topological domains in order to bridge the gap between Topological Deep Learning (TDL) and other types of structured datasets (e.g. point clouds, graphs). Specifically, participants were asked to design and implement topological liftings, i.e. mappings between different data structures and topological domains --like hypergraphs, or simplicial/cell/combinatorial complexes. The challenge received 52 submissions satisfying all the requirements. This paper introduces the main scope of the challenge, and summarizes the main results and findings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05211 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain Bernárdez, Guillermo Telyatnikov, Lev Montagna, Marco Baccini, Federica Papillon, Mathilde Ferriol-Galmés, Miquel Hajij, Mustafa Papamarkou, Theodore Bucarelli, Maria Sofia Zaghen, Olga Mathe, Johan Myers, Audun Mahan, Scott Lillemark, Hansen Vadgama, Sharvaree Bekkers, Erik Doster, Tim Emerson, Tegan Kvinge, Henry Agate, Katrina Ahmed, Nesreen K Bai, Pengfei Banf, Michael Battiloro, Claudio Beketov, Maxim Bogdan, Paul Carrasco, Martin Cavallo, Andrea Choi, Yun Young Dasoulas, George Elphick, Matouš Escalona, Giordan Filipiak, Dominik Fritze, Halley Gebhart, Thomas Gil-Sorribes, Manel Goomanee, Salvish Guallar, Victor Imasheva, Liliya Irimia, Andrei Jin, Hongwei Johnson, Graham Kanakaris, Nikos Koloski, Boshko Kovač, Veljko Lecha, Manuel Lee, Minho Leroy, Pierrick Long, Theodore Magai, German Martinez, Alvaro Masden, Marissa Mežnar, Sebastian Miquel-Oliver, Bertran Molina, Alexis Nikitin, Alexander Nurisso, Marco Piekenbrock, Matt Qin, Yu Rygiel, Patryk Salatiello, Alessandro Schattauer, Max Snopov, Pavel Suk, Julian Sánchez, Valentina Tec, Mauricio Vaccarino, Francesco Verhellen, Jonas Wantiez, Frederic Weers, Alexander Zajec, Patrik Škrlj, Blaž Miolane, Nina Machine Learning Artificial Intelligence This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem of representing data in different discrete topological domains in order to bridge the gap between Topological Deep Learning (TDL) and other types of structured datasets (e.g. point clouds, graphs). Specifically, participants were asked to design and implement topological liftings, i.e. mappings between different data structures and topological domains --like hypergraphs, or simplicial/cell/combinatorial complexes. The challenge received 52 submissions satisfying all the requirements. This paper introduces the main scope of the challenge, and summarizes the main results and findings. |
| title | ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2409.05211 |