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pro vyhledávání: '"Agerberg, Jens"'
Autor:
Hajij, Mustafa, Papillon, Mathilde, Frantzen, Florian, Agerberg, Jens, AlJabea, Ibrahem, Ballester, Ruben, Battiloro, Claudio, Bernárdez, Guillermo, Birdal, Tolga, Brent, Aiden, Chin, Peter, Escalera, Sergio, Fiorellino, Simone, Gardaa, Odin Hoff, Gopalakrishnan, Gurusankar, Govil, Devendra, Hoppe, Josef, Karri, Maneel Reddy, Khouja, Jude, Lecha, Manuel, Livesay, Neal, Meißner, Jan, Mukherjee, Soham, Nikitin, Alexander, Papamarkou, Theodore, Prílepok, Jaro, Ramamurthy, Karthikeyan Natesan, Rosen, Paul, Guzmán-Sáenz, Aldo, Salatiello, Alessandro, Samaga, Shreyas N., Scardapane, Simone, Schaub, Michael T., Scofano, Luca, Spinelli, Indro, Telyatnikov, Lev, Truong, Quang, Walters, Robin, Yang, Maosheng, Zaghen, Olga, Zamzmi, Ghada, Zia, Ali, Miolane, Nina
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. To
Externí odkaz:
http://arxiv.org/abs/2402.02441
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
Distances have a ubiquitous role in persistent homology, from the direct comparison of homological representations of data to the definition and optimization of invariants. In this article we introduce a family of parametrized pseudometrics between p
Externí odkaz:
http://arxiv.org/abs/2301.06484
Homology-based invariants can be used to characterize the geometry of datasets and thereby gain some understanding of the processes generating those datasets. In this work we investigate how the geometry of a dataset changes when it is subsampled in
Externí odkaz:
http://arxiv.org/abs/2203.08306
Autor:
Agerberg, Jens
Stable rank has recently been proposed as an invariant to encode the result of persistent homology, a method used in topological data analysis. In this thesis we develop methods for statistical analysis as well as machine learning methods based on st
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273581
Akademický článek
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Autor:
Agerberg, Jens
Stable rank has recently been proposed as an invariant to encode the result of persistent homology, a method used in topological data analysis. In this thesis we develop methods for statistical analysis as well as machine learning methods based on st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::0c95b81e24a9194e13fab01cdff4a6fc
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273581
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273581