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Séminaire ALGO : Léo Pavlet Salomon (GREYC), « Groupe fondamental et pavages du plan: quelques constructions ».
Séminaire ALGO : Léo Pavlet Salomon (GREYC), « Groupe fondamental et pavages du plan: quelques constructions ».
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, … Continue reading Séminaire ALGO : Léo Pavlet Salomon (GREYC), « Groupe fondamental et pavages du plan: quelques constructions ».
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Séminaire ALGO : Olivier Bournez (LIX, Ecole Polytechnique), « Computing with analog models. Computing with ordinary differential equations ».
Séminaire ALGO : Olivier Bournez (LIX, Ecole Polytechnique), « Computing with analog models. Computing with ordinary differential equations ».
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 … Continue reading Séminaire ALGO : Olivier Bournez (LIX, Ecole Polytechnique), « Computing with analog models. Computing with ordinary differential equations ».
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Séminaire IMAGE : Quentin Bertrand (MILA), « Hyperparameter selection for high dimensional sparse learning: application to neuro-imaging »
Séminaire IMAGE : Quentin Bertrand (MILA), « Hyperparameter selection for high dimensional sparse learning: application to neuro-imaging »
Speaker: Quentin Bertrand (https://qb3.github.io/) Abstract: Due 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 … Continue reading Séminaire IMAGE : Quentin Bertrand (MILA), « Hyperparameter selection for high dimensional sparse learning: application to neuro-imaging »
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