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S³ : Signal Processing seminar @ Paris-Saclay

The goal of the S³ Seminar is to welcome recognized researchers, but also post-docs and PhD students, around the field of signal processing and its applications. It is open to everyone and is usually hosted every Friday morning at 11:00 am, “Salle des séminaires du L2S” Room C4.01, Breguet Building, CentraleSupélec.

Organizers

Florent Bouchard and François Orieux.

Previous organizers


24/09/2021

Robust low-rank covariance matrix estimation with missing values and application to classification problems

Speaker — Alexandre Hippert-Ferrer (L2S, CentraleSupelec, Univ. Paris-Saclay) Abstract — Missing values are inherent to real-world data sets. Statistical learning […]
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21/05/2021

Online estimation of elliptical distributions and their mixture: the component-wise information gradient method

Speaker — Jialun Zhou (IMS, Groupe Signal Image — CNRS, Université de Bordeaux) https://s3-seminar.github.io/seminars/jialun-zhou/ Abstract — Elliptically-Contoured Distributions (ECD) and […]
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12/03/2021

Speeding up of kernel-based learning for high-order tensor

Speaker — Ouafae Karmouda (SIGMA team at CRIStAL laboratory, Lille, France) Abstract — Supervised learning is a major task to […]
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05/03/2021

Sampling rates for l1 synthesis

Speaker — Claire Boyer (Sorbonne Université) Abstract — This work investigates the problem of signal recovery from undersampled noisy sub-Gaussian […]
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11/12/2020

Efficient MCMC sampling via asymptotically exact data augmentation

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. […]
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27/11/2020

Riemannian geometry for data analysis: illustration on blind source separation and low-rank structured covariance matrices

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 […]
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