Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Reise, Wojciech"'
Autor:
Reise, Wojciech, Fernández, Ximena, Dominguez, Maria, Harrington, Heather A., Beguerisse-Díaz, Mariano
We present a topological audio fingerprinting approach for robustly identifying duplicate audio tracks. Our method applies persistent homology on local spectral decompositions of audio signals, using filtered cubical complexes computed from mel-spect
Externí odkaz:
http://arxiv.org/abs/2309.03516
We present a method to construct signatures of periodic-like data. Based on topological considerations, our construction encodes information about the order and values of local extrema. Its main strength is robustness to reparametrisation of the obse
Externí odkaz:
http://arxiv.org/abs/2306.13453
We consider a signal composed of several periods of a periodic function, of which we observe a noisy reparametrisation. The phase estimation problem consists of finding that reparametrisation, and, in particular, the number of observed periods. Exist
Externí odkaz:
http://arxiv.org/abs/2205.14390
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:
Tauzin, Guillaume, Lupo, Umberto, Tunstall, Lewis, Pérez, Julian Burella, Caorsi, Matteo, Reise, Wojciech, Medina-Mardones, Anibal, Dassatti, Alberto, Hess, Kathryn
Publikováno v:
NeurIPS 2020 workshop "Topological Data Analysis and beyond" (https://openreview.net/forum?id=fjQtZJOCTXf); JMLR 22 (https://www.jmlr.org/papers/v22/20-325.html)
We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations. The library's ability to handle various types of da
Externí odkaz:
http://arxiv.org/abs/2004.02551
Akademický článek
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