Speaker — Maxime Vono (IRIT — INP-ENSEEIHT) https://s3-seminar.github.io/seminars/maxime-vono/ Abstract — Performing exact Bayesian inference for complex models is computationally intractable. Markov chain Monte Carlo (MCMC) algorithms can provide reliable approximations of the posterior distribution but are expensive for large datasets and high-dimensional models. A standard approach to mitigate this complexity consists in using subsampling techniques […]
08/12/2020 – 14h00-15h00 – Online The multiplicity-induced-dominancy property for scalar differential equations with time-delays Guilherme Mazanti (INRIA équipe DISCO, L2S, CentraleSupélec) Abstract. Even in simple situations with time-delays such as that of linear equations with constant coefficients and constant delays, the spectral analysis of time-delay systems can be a challenging question. Indeed, contrarily to the […]
Paul Verrax, PhD student at L2S and SuperGrid Institute. Tuesday 8th December at 11:00 via Microsoft Teams at Join Microsoft Teams MeetingLearn more about Teams Abstract: The emergence of meshed HVDC grids is seen as a promising option to interconnect large amount of renewable energies over long distances. The protection of such grids requires the fast […]
The nineteenth UQSay seminar on UQ, DACE and related topics, organized by L2S, MSSMAT, LMT and EDF R&D, will take place online on Thursday afternoon, December 3, 2020. 14h–15h — Álvaro Rollón de Pinedo (EDF R&D and Université Grenoble Alpes) Functional outlier detection applied to nuclear transient simulation analysis The ever increasing recording and storing […]
Speaker — Florent Bouchard (L2S — CNRS, Université Paris-Saclay, CentraleSupélec) https://s3-seminar.github.io/seminars/florent-bouchard/ Abstract — In this presentation, Riemannian geometry for data analysis is introduced. In particular, it is applied on two specific statistical signal processing problems: blind source separation and low-rank structured covariance matrices. Blind source separation can be solved by jointly diagonalizing some covariance matrices. […]
