Évènements à venir

An overview of cointegration tests in the time and frequency domains

Speaker — Igor Viveiros Melo Souza (Federal University of Belo Horizonte, Brazil) Abstract — Cointegrated and non-cointegrated processes from economic and econometric point of view, based on the time and frequency domain will be presented. Standard tests for cointegrated times series will be discussed as well as their advantages and drawbacks in financial area. In […]


Vascular networks, from low-level vision to generative models

Speaker — Hugues Talbot (CVN, CentraleSupélec, INRIA, Université Paris-Saclay) Abstract — The study of vascular networks is important in medicalimaging because disease affecting blood vessels is the first cause ofmortality and morbidity in the Western world. Yet, surprisingly, theses studies have not been the subject of majorresearch efforts. From the low-level vision point of view, […]


Computational characterization of supra-threshold hearing to understand speech-in-noise intelligibility deficits

Speaker — Emmanuel Ponsot (Laboratoire des Systèmes Perceptifs, ENS) Abstract — A largely unresolved problem in hearing sciences concerns the large heterogeneity observed among individuals with similar audiograms (hearing thresholds measured in quiet) in understanding speech in noisy environments. Recent studies suggest that supra-threshold auditory mechanisms (i.e. that operate above detection threshold) play a prominent […]


Test d’hypothèses bayésien non paramétrique et application à la modélisation de la zone du langage

Speaker — Diarra Fall (Institut Denis Poisson, UMR CNRS, Université d’Orléans et de Tours) Abstract — Dans cet exposé, je parlerai de modèles bayésiens non paramétriques et de tests d’hypothèses, avec pour exemple d’application, un travail en cours avec le centre hospitalier régional d’Orléans portant sur l’estimation de la zone contrôlant le langage chez des […]


Safe squeezing for antisparse coding

Speaker — Clément Elvira (PANAMA research group, Iniria, CNRS, IRISA – Rennes) Abstract — Spreading the information over all coefficients of a representation is a desirableproperty in many applications such as digital communication or machine learning. Thisso-called antisparse representation can be obtained by solving a convex program involving a$\ell_\infty$-norm penalty combined with a quadratic discrepancy. […]