Zobrazeno 1 - 10
of 524
pro vyhledávání: '"ROZET, FRANÇOIS"'
Autor:
Ohana, Ruben, McCabe, Michael, Meyer, Lucas, Morel, Rudy, Agocs, Fruzsina J., Beneitez, Miguel, Berger, Marsha, Burkhart, Blakesley, Dalziel, Stuart B., Fielding, Drummond B., Fortunato, Daniel, Goldberg, Jared A., Hirashima, Keiya, Jiang, Yan-Fei, Kerswell, Rich R., Maddu, Suryanarayana, Miller, Jonah, Mukhopadhyay, Payel, Nixon, Stefan S., Shen, Jeff, Watteaux, Romain, Blancard, Bruno Régaldo-Saint, Rozet, François, Parker, Liam H., Cranmer, Miles, Ho, Shirley
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the effi
Externí odkaz:
http://arxiv.org/abs/2412.00568
Diffusion models recently proved to be remarkable priors for Bayesian inverse problems. However, training these models typically requires access to large amounts of clean data, which could prove difficult in some settings. In this work, we present a
Externí odkaz:
http://arxiv.org/abs/2405.13712
Autor:
Rozet, François, Louppe, Gilles
Data assimilation addresses the problem of identifying plausible state trajectories of dynamical systems given noisy or incomplete observations. In geosciences, it presents challenges due to the high-dimensionality of geophysical dynamical systems, o
Externí odkaz:
http://arxiv.org/abs/2310.01853
Autor:
Rozet, François, Louppe, Gilles
Data assimilation, in its most comprehensive form, addresses the Bayesian inverse problem of identifying plausible state trajectories that explain noisy or incomplete observations of stochastic dynamical systems. Various approaches have been proposed
Externí odkaz:
http://arxiv.org/abs/2306.10574
Autor:
Vasist, Malavika, Rozet, François, Absil, Olivier, Mollière, Paul, Nasedkin, Evert, Louppe, Gilles
Publikováno v:
A&A 672, A147 (2023)
Retrieving the physical parameters from spectroscopic observations of exoplanets is key to understanding their atmospheric properties. Exoplanetary atmospheric retrievals are usually based on approximate Bayesian inference and rely on sampling-based
Externí odkaz:
http://arxiv.org/abs/2301.06575
Autor:
Rozet, François1 (AUTHOR) francois.rozet@imm.fr, Hennequin, Christophe2,3 (AUTHOR), Mongiat-Artus, Pierre3,4 (AUTHOR), Pello-Leprince-Ringuet, Nathalie5 (AUTHOR), Grandoulier, Anne-Sophie5 (AUTHOR), Roupret, Morgan6 (AUTHOR)
Publikováno v:
Aging Male. Dec2024, Vol. 27 Issue 1, p1-13. 13p.
Modern approaches for simulation-based inference rely upon deep learning surrogates to enable approximate inference with computer simulators. In practice, the estimated posteriors' computational faithfulness is, however, rarely guaranteed. For exampl
Externí odkaz:
http://arxiv.org/abs/2208.13624
Autor:
Ploussard, Guillaume, Barret, Eric, Fiard, Gaëlle, Lenfant, Louis, Malavaud, Bernard, Giannarini, Gianluca, Almeras, Christophe, Aziza, Richard, Renard-Penna, Raphaële, Descotes, Jean-Luc, Rozet, François, Beauval, Jean-Baptiste, Salin, Ambroise, Rouprêt, Morgan
Publikováno v:
In European Urology Oncology October 2024 7(5):1080-1087
Autor:
Hermans, Joeri, Delaunoy, Arnaud, Rozet, François, Wehenkel, Antoine, Begy, Volodimir, Louppe, Gilles
We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms can produce computationally unfaithful posterior approximations. Our results show that all benchmarked algorithms -- (Sequential) Neural Poste
Externí odkaz:
http://arxiv.org/abs/2110.06581
Autor:
Rozet, François, Louppe, Gilles
In many areas of science, complex phenomena are modeled by stochastic parametric simulators, often featuring high-dimensional parameter spaces and intractable likelihoods. In this context, performing Bayesian inference can be challenging. In this wor
Externí odkaz:
http://arxiv.org/abs/2110.00449