Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Mathe, Johan"'
Autor:
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
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
Externí odkaz:
http://arxiv.org/abs/2409.05211
Autor:
Sanborn, Sophia, Mathe, Johan, Papillon, Mathilde, Buracas, Domas, Lillemark, Hansen J, Shewmake, Christian, Bertics, Abby, Pennec, Xavier, Miolane, Nina
The enduring legacy of Euclidean geometry underpins classical machine learning, which, for decades, has been primarily developed for data lying in Euclidean space. Yet, modern machine learning increasingly encounters richly structured data that is in
Externí odkaz:
http://arxiv.org/abs/2407.09468
An important problem in signal processing and deep learning is to achieve \textit{invariance} to nuisance factors not relevant for the task. Since many of these factors are describable as the action of a group $G$ (e.g. rotations, translations, scali
Externí odkaz:
http://arxiv.org/abs/2407.07655
Autor:
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
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 topo
Externí odkaz:
http://arxiv.org/abs/2309.15188
Autor:
Myers, Adele, Utpala, Saiteja, Talbar, Shubham, Sanborn, Sophia, Shewmake, Christian, Donnat, Claire, Mathe, Johan, Lupo, Umberto, Sonthalia, Rishi, Cui, Xinyue, Szwagier, Tom, Pignet, Arthur, Bergsson, Andri, Hauberg, Soren, Nielsen, Dmitriy, Sommer, Stefan, Klindt, David, Hermansen, Erik, Vaupel, Melvin, Dunn, Benjamin, Xiong, Jeffrey, Aharony, Noga, Pe'er, Itsik, Ambellan, Felix, Hanik, Martin, Nava-Yazdani, Esfandiar, von Tycowicz, Christoph, Miolane, Nina
This paper presents the computational challenge on differential geometry and topology that was hosted within the ICLR 2022 workshop ``Geometric and Topological Representation Learning". The competition asked participants to provide implementations of
Externí odkaz:
http://arxiv.org/abs/2206.09048
Autor:
Miolane, Nina, Caorsi, Matteo, Lupo, Umberto, Guerard, Marius, Guigui, Nicolas, Mathe, Johan, Cabanes, Yann, Reise, Wojciech, Davies, Thomas, Leitão, António, Mohapatra, Somesh, Utpala, Saiteja, Shailja, Shailja, Corso, Gabriele, Liu, Guoxi, Iuricich, Federico, Manolache, Andrei, Nistor, Mihaela, Bejan, Matei, Nicolicioiu, Armand Mihai, Luchian, Bogdan-Alexandru, Stupariu, Mihai-Sorin, Michel, Florent, Duc, Khanh Dao, Abdulrahman, Bilal, Beketov, Maxim, Maignant, Elodie, Liu, Zhiyuan, Černý, Marek, Bauw, Martin, Velasco-Forero, Santiago, Angulo, Jesus, Long, Yanan
This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning". The competition asked participants to provide creative contribution
Externí odkaz:
http://arxiv.org/abs/2108.09810
Autor:
Miolane, Nina, Brigant, Alice Le, Mathe, Johan, Hou, Benjamin, Guigui, Nicolas, Thanwerdas, Yann, Heyder, Stefan, Peltre, Olivier, Koep, Niklas, Zaatiti, Hadi, Hajri, Hatem, Cabanes, Yann, Gerald, Thomas, Chauchat, Paul, Shewmake, Christian, Kainz, Bernhard, Donnat, Claire, Holmes, Susan, Pennec, Xavier
We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-o
Externí odkaz:
http://arxiv.org/abs/2004.04667
Photovoltaic (PV) power generation has emerged as one of the lead renewable energy sources. Yet, its production is characterized by high uncertainty, being dependent on weather conditions like solar irradiance and temperature. Predicting PV productio
Externí odkaz:
http://arxiv.org/abs/1902.01453
We introduce geomstats, a python package that performs computations on manifolds such as hyperspheres, hyperbolic spaces, spaces of symmetric positive definite matrices and Lie groups of transformations. We provide efficient and extensively unit-test
Externí odkaz:
http://arxiv.org/abs/1805.08308
Autor:
Myers, Adele, Utpala, Saiteja, Talbar, Shubham, Sanborn, Sophia, Shewmake, Christian, Donnat, Claire, Mathe, Johan, Lupo, Umberto, Sonthalia, Rishi, Cui, Xinyue, Szwagier, Tom, Pignet, Arthur, Bergsson, Andri, Hauberg, Soren, Nielsen, Dmitriy, Sommer, Stefan, Klindt, David, Hermansen, Erik, Vaupel, Melvin, Dunn, Benjamin, Xiong, Jeffrey, Aharony, Noga, Pe'Er, Itsik, Ambellan, Felix, Hanik, Martin, Nava-Yazdani, Esfandiar, von Tycowicz, Christoph, Miolane, Nina
Publikováno v:
Proceedings of Machine Learning Research
Proceedings of Machine Learning Research, 2022, Topological, Algebraic and Geometric Learning Workshops 2022, 196, pp.269-276. ⟨10.5281/zenodo.6554616⟩
Proceedings of Machine Learning Research, 2022, Topological, Algebraic and Geometric Learning Workshops 2022, 196, pp.269-276. ⟨10.5281/zenodo.6554616⟩
International audience; This paper presents the computational challenge on differential geometry and topology that was hosted within the ICLR 2022 workshop “Geometric and Topo- logical Representation Learning”. The competition asked participants
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d20e248899540b2acfa94fe77e7c8457
https://hal.science/hal-03903044
https://hal.science/hal-03903044