Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2406.06642 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915462367936512 |
|---|---|
| author | Telyatnikov, Lev Bernardez, Guillermo Montagna, Marco Hajij, Mustafa Carrasco, Martin Vasylenko, Pavlo Papillon, Mathilde Zamzmi, Ghada Schaub, Michael T. Verhellen, Jonas Snopov, Pavel Miquel-Oliver, Bertran Gil-Sorribes, Manel Molina, Alexis Guallar, Victor Long, Theodore Suk, Julian Rygiel, Patryk Nikitin, Alexander Escalona, Giordan Banf, Michael Filipiak, Dominik Schattauer, Max Imasheva, Liliya Martinez, Alvaro Fritze, Halley Masden, Marissa Sánchez, Valentina Lecha, Manuel Cavallo, Andrea Battiloro, Claudio Piekenbrock, Matt Tec, Mauricio Dasoulas, George Miolane, Nina Scardapane, Simone Papamarkou, Theodore |
| author_facet | Telyatnikov, Lev Bernardez, Guillermo Montagna, Marco Hajij, Mustafa Carrasco, Martin Vasylenko, Pavlo Papillon, Mathilde Zamzmi, Ghada Schaub, Michael T. Verhellen, Jonas Snopov, Pavel Miquel-Oliver, Bertran Gil-Sorribes, Manel Molina, Alexis Guallar, Victor Long, Theodore Suk, Julian Rygiel, Patryk Nikitin, Alexander Escalona, Giordan Banf, Michael Filipiak, Dominik Schattauer, Max Imasheva, Liliya Martinez, Alvaro Fritze, Halley Masden, Marissa Sánchez, Valentina Lecha, Manuel Cavallo, Andrea Battiloro, Claudio Piekenbrock, Matt Tec, Mauricio Dasoulas, George Miolane, Nina Scardapane, Simone Papamarkou, Theodore |
| contents | This work introduces TopoBench, an open-source library designed to standardize benchmarking and accelerate research in topological deep learning (TDL). TopoBench decomposes TDL into a sequence of independent modules for data generation, loading, transforming and processing, as well as model training, optimization and evaluation. This modular organization provides flexibility for modifications and facilitates the adaptation and optimization of various TDL pipelines. A key feature of TopoBench is its support for transformations and lifting across topological domains. Mapping the topology and features of a graph to higher-order topological domains, such as simplicial and cell complexes, enables richer data representations and more fine-grained analyses. The applicability of TopoBench is demonstrated by benchmarking several TDL architectures across diverse tasks and datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06642 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TopoBench: A Framework for Benchmarking Topological Deep Learning Telyatnikov, Lev Bernardez, Guillermo Montagna, Marco Hajij, Mustafa Carrasco, Martin Vasylenko, Pavlo Papillon, Mathilde Zamzmi, Ghada Schaub, Michael T. Verhellen, Jonas Snopov, Pavel Miquel-Oliver, Bertran Gil-Sorribes, Manel Molina, Alexis Guallar, Victor Long, Theodore Suk, Julian Rygiel, Patryk Nikitin, Alexander Escalona, Giordan Banf, Michael Filipiak, Dominik Schattauer, Max Imasheva, Liliya Martinez, Alvaro Fritze, Halley Masden, Marissa Sánchez, Valentina Lecha, Manuel Cavallo, Andrea Battiloro, Claudio Piekenbrock, Matt Tec, Mauricio Dasoulas, George Miolane, Nina Scardapane, Simone Papamarkou, Theodore Machine Learning This work introduces TopoBench, an open-source library designed to standardize benchmarking and accelerate research in topological deep learning (TDL). TopoBench decomposes TDL into a sequence of independent modules for data generation, loading, transforming and processing, as well as model training, optimization and evaluation. This modular organization provides flexibility for modifications and facilitates the adaptation and optimization of various TDL pipelines. A key feature of TopoBench is its support for transformations and lifting across topological domains. Mapping the topology and features of a graph to higher-order topological domains, such as simplicial and cell complexes, enables richer data representations and more fine-grained analyses. The applicability of TopoBench is demonstrated by benchmarking several TDL architectures across diverse tasks and datasets. |
| title | TopoBench: A Framework for Benchmarking Topological Deep Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.06642 |