Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Morel, Rudy"'
Autor:
Golkar, Siavash, Bietti, Alberto, Pettee, Mariel, Eickenberg, Michael, Cranmer, Miles, Hirashima, Keiya, Krawezik, Geraud, Lourie, Nicholas, McCabe, Michael, Morel, Rudy, Ohana, Ruben, Parker, Liam Holden, Blancard, Bruno Régaldo-Saint, Cho, Kyunghyun, Ho, Shirley
Transformers have revolutionized machine learning across diverse domains, yet understanding their behavior remains crucial, particularly in high-stakes applications. This paper introduces the contextual counting task, a novel toy problem aimed at enh
Externí odkaz:
http://arxiv.org/abs/2406.02585
Cascades of events and extreme occurrences have garnered significant attention across diverse domains such as financial markets, seismology, and social physics. Such events can stem either from the internal dynamics inherent to the system (endogenous
Externí odkaz:
http://arxiv.org/abs/2404.16467
We introduce a Path Shadowing Monte-Carlo method, which provides prediction of future paths, given any generative model. At any given date, it averages future quantities over generated price paths whose past history matches, or `shadows', the actual
Externí odkaz:
http://arxiv.org/abs/2308.01486
Publikováno v:
PNAS Nexus, Volume 3, Issue 4, April 2024, pgae103
Physicists routinely need probabilistic models for a number of tasks such as parameter inference or the generation of new realizations of a field. Establishing such models for highly non-Gaussian fields is a challenge, especially when the number of s
Externí odkaz:
http://arxiv.org/abs/2306.17210
Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders
Autor:
Siahkoohi, Ali, Morel, Rudy, Balestriero, Randall, Allys, Erwan, Sainton, Grégory, Kawamura, Taichi, de Hoop, Maarten V.
Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only access to a dataset of signal mixtures. This problem is inherently ill-po
Externí odkaz:
http://arxiv.org/abs/2305.16189
Autor:
Siahkoohi, Ali, Morel, Rudy, de Hoop, Maarten V., Allys, Erwan, Sainton, Grégory, Kawamura, Taichi
Source separation involves the ill-posed problem of retrieving a set of source signals that have been observed through a mixing operator. Solving this problem requires prior knowledge, which is commonly incorporated by imposing regularity conditions
Externí odkaz:
http://arxiv.org/abs/2301.11981
Autor:
Morel, Rudy, Rochette, Gaspar, Leonarduzzi, Roberto, Bouchaud, Jean-Philippe, Mallat, Stéphane
We introduce the wavelet scattering spectra which provide non-Gaussian models of time-series having stationary increments. A complex wavelet transform computes signal variations at each scale. Dependencies across scales are captured by the joint corr
Externí odkaz:
http://arxiv.org/abs/2204.10177
Akademický článek
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Akademický článek
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Autor:
Morel, Rudy, Rochette, Gaspar, Leonarduzzi, Roberto, Bouchaud, Jean-Philippe, Mallat, Stéphane
We introduce a scattering covariance matrix which provides non-Gaussian models of time-series having stationary increments. A complex wavelet transform computes signal variations at each scale. Dependencies across scales are captured by the joint cov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::a95ccee46aaacc7c0d422d6618c3298a
https://hal.science/hal-03656045
https://hal.science/hal-03656045