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PRODID:-//GREYC UMR CNRS 6072 - Groupe de Recherche en Informatique, Image, et Instrumentation de Caen - ECPv5.7.0//NONSGML v1.0//EN
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X-WR-CALNAME:GREYC UMR CNRS 6072 - Groupe de Recherche en Informatique, Image, et Instrumentation de Caen
X-ORIGINAL-URL:https://www.greyc.fr
X-WR-CALDESC:évènements pour GREYC UMR CNRS 6072 - Groupe de Recherche en Informatique, Image, et Instrumentation de Caen
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TZID:Europe/Paris
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TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20210328T010000
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DTSTART:20211031T010000
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BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20211109T100000
DTEND;TZID=Europe/Paris:20211109T110000
DTSTAMP:20260825T232501
CREATED:20211108T102835Z
LAST-MODIFIED:20211108T102835Z
UID:10637-1636452000-1636455600@www.greyc.fr
SUMMARY:Séminaire ALGO : Léo Pavlet Salomon (GREYC)\, "Groupe fondamental et pavages du plan: quelques constructions".
DESCRIPTION:Résumé : On appelle sous-shift (ou sous-décalage) un ensemble de pavages ou de coloriages du plan respectant certaines contraintes locales. Historiquement introduits comme discrétisations de systèmes dynamiques continus\, on se propose ici d’en étudier un invariant topologique\, introduit par W.Geller et J.Propp\, le Groupe Fondamental Projectif. A l’instar de la définition habituelle du groupe fondamental\, un invariant d’espaces topologiques\, il s’agira ici de comprendre comment l’on peut définir une notion de chemins dans les pavages\, reliant les configurations entre elles\, et d’étudier comment les déformations de ces chemins permettent d’associer à chaque pavage un unique objet algébrique: son groupe fondamental projectif. En particulier\, on montrera dans cet exposé comment réaliser une large classe de groupes comme groupes fondamentaux de certains pavages.
URL:https://www.greyc.fr/event/seminaire-algo-leo-pavlet-salomon-greyc-groupe-fondamental-et-pavages-du-plan-quelques-constructions/
LOCATION:Sciences 3- S3 351
CATEGORIES:Amacc,Séminaire Algo
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BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20211116T100000
DTEND;TZID=Europe/Paris:20211116T110000
DTSTAMP:20260825T232501
CREATED:20211116T093504Z
LAST-MODIFIED:20211116T093504Z
UID:10645-1637056800-1637060400@www.greyc.fr
SUMMARY:Séminaire ALGO : Olivier Bournez (LIX\, Ecole Polytechnique)\, "Computing with analog models. Computing with ordinary differential equations".
DESCRIPTION:Abstract : Differential equations is some universal language in many contexts\, and in particular in experimental sciences.  Motivated initially by analog models of computation\, we will review various results demonstrating that it is possible to program with ordinary differential equations\, or their discrete counterpart\, discrete differences. We will show that several concepts from computability and complexity theory can be formulated in this framework. We will hence present some applications to bioinformatics\, computer algebra\, or logic.
URL:https://www.greyc.fr/event/seminaire-algo-olivier-bournez-lix-ecole-polytechnique-computing-with-analog-models-computing-with-ordinary-differential-equations/
CATEGORIES:Amacc,Séminaire Algo
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BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20211118T153000
DTEND;TZID=Europe/Paris:20211118T163000
DTSTAMP:20260825T232501
CREATED:20211111T095606Z
LAST-MODIFIED:20230307T085652Z
UID:10641-1637249400-1637253000@www.greyc.fr
SUMMARY:Séminaire IMAGE : Quentin Bertrand (MILA)\, « Hyperparameter selection for high dimensional sparse learning: application to neuro-imaging »
DESCRIPTION:Speaker: \nQuentin Bertrand (https://qb3.github.io/) \nAbstract:\nDue to non-invasiveness and excellent time resolution\, magneto- and electroencephalography (M/EEG) have emerged as tools of choice to monitor brain activity. Reconstructing brain signals from M/EEG measurements is a high dimensional ill-posed inverse problem. Typical estimators of brain signals involve challenging optimization problems\, composed of the sum of a data-fidelity term\, and a sparsity promoting term. Because of their notoriously hard to tune regularization hyperparameters\, sparsity-based estimators are currently not massively used by neuroscientists. \nDuring this talk I will talk about two aspects of the brain source reconstruction problem:\n– State of the art solvers for the source localization problem include coordinate descent\, which is notoriously hard to accelerate in practice. I will introduce an effective way to speed it up in theory and practice.\n– Then I will focus on the hyperparameter selection and investigate hyperparameter optimization. It requires tackling bilevel optimization with nonsmooth inner problems. Such problems are canonically solved using zeros order techniques\, such as grid-search or random-search. I will present a more efficient technique to solve these challenging bilevel optimization problems using first-order methods.
URL:https://www.greyc.fr/event/seminaire-image-quentin-bertrand-mila-hyperparameter-selection-for-high-dimensional-sparse-learning-application-to-neuro-imaging/
LOCATION:En distanciel
CATEGORIES:General,Image,Seminaire Image
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