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X-WR-CALNAME;VALUE=TEXT: -- Institut de Mathématiques de Marseille\, UMR 7373
X-WR-RELCALID:http://www.i2m.univ-amu.fr/spip.php?page=article&id_article=0
BEGIN:VEVENT
SUMMARY:Sixin ZHANG - Statistical model of non-Gaussian process with wavelet scattering moments
UID:20180517T154237-a128-e2349@https://www.i2m.univ-amu.fr
DTSTAMP:20180517T154237
DTSTART:20180525T140000
DTEND:20180525T150000
CREATED:20180517T154237
ATTENDEE;CN=Sixin ZHANG:mailto:no-reply@math.cnrs.fr
LAST-MODIFIED:20180517T154237
LOCATION:CMI\, salle de séminaire R164 (1er étage)
DESCRIPTION:One of the most challenging problems in statistical modeling is to define a minimal set of statistics so as to infer a stochastic model from few observational data of the underlying random process. We propose such set of statistics based on the wavelet scattering transform. Our goal is to model the non-Gaussianarity and the long-range interaction of the data\, in particular when there is complex geometry and transient structures at multiple scales such as Turbulence. We follow the maximum entropy principle to infer a stochastic model given a set of statistical moment constraints. It results in a Gibbs distribution which is common in statistical physics to describe the equilibrium states. In this talk\, I will discuss the current state-of-art methods to model the texture as a stationary and ergodic random process\, including convolutional neural network based approach. We compare different methods quantitatively by estimating the power spectrum\, and the entropy of the random process. Numerical results on isotropic Turbulence will be presented. http://www.di.ens.fr/~zhang/ Sixin ZHANG [
CATEGORIES:(Séminaire Signal et Apprentissage|textebrut|filtrer_ical)]
URL:http://www.i2m.univ-amu.fr/Seminaire-Signal-et-Apprentissage?id_evenement=2349
SEQUENCE:0
STATUS:CONFIRMED
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