BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Département de mathématiques et applications - ECPv6.2.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Département de mathématiques et applications
X-ORIGINAL-URL:https://www.math.ens.psl.eu
X-WR-CALDESC:évènements pour Département de mathématiques et applications
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260317T120000
DTEND;TZID=Europe/Paris:20260317T130000
DTSTAMP:20260406T224820
CREATED:20260303T150022Z
LAST-MODIFIED:20260312T111711Z
UID:21046-1773748800-1773752400@www.math.ens.psl.eu
SUMMARY:ENS-Data Science colloquium - Julien Mairal : Physical Models and Machine Learning for Scientific Imaging
DESCRIPTION:In this presentation\, we will present a few scientific imaging problems where hybrid approaches that combine physical models of image formation and deep learning are highly successful. We will then address a fundamental challenge in image restoration: the choice of estimator\, as perceptual quality often does not align with traditional objective criteria such as minimizing the mean squared error. Finally\, we will show how algorithms related to diffusion—highly successful in generative image modeling—can provide an effective solution to this problem. \n\n\n\n  \n\n\n\nThese seminars are being made possible through the support of the CFM-ENS Chair « Modèles et Sciences des Données ». \n\n\n\nThe organizers: Giulio Biroli\, Alex Cayco Gajic\, Bruno Loureiro\, Stéphane Mallat\, Gabriel Peyré.
URL:https://www.math.ens.psl.eu/evenement/ens-data-science-colloquium-julien-mairal-physical-models-and-machine-learning-for-scientific-imaging/
LOCATION:ENS Salle Dussane
CATEGORIES:ENS-Data Science colloquium
END:VEVENT
END:VCALENDAR