Notes and Comments on S. Mallat’s Lectures at Collège de France (2021) - Archive ouverte HAL Accéder directement au contenu
Cours Année : 2021

Notes and Comments on S. Mallat’s Lectures at Collège de France (2021)

Résumé

The 2021 course by Stéphane Mallat, Professor at the Collège de France, focuses on exploring the triptych of 'Regularity, Approximation, Sparsity.' While the courses since 2018 have focused on deep neural networks, this year is oriented towards signal processing (data). Two classical themes are addressed: approximation in low dimension and denoising/compression. These themes are not far from those of statistical learning because we need to consider sparse representations that exploit the regularities of the signal (the data). Topics reviewed include Fourier analysis, Shannon's theorem, and multiresolution analyses by wavelets with an application to JPEG and JPEG2000 image compression.
Fichier principal
Vignette du fichier
Resume-2021_EN.pdf (3.64 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04550739 , version 1 (18-04-2024)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

  • HAL Id : hal-04550739 , version 1

Citer

Jean-Eric Campagne. Notes and Comments on S. Mallat’s Lectures at Collège de France (2021): Multiscale Models and Convolutional Neural Networks. Master. Sparse representations, https://www.college-de-france.fr/fr/agenda/cours/representations-parcimonieuses, France. 2021, pp.132. ⟨hal-04550739⟩
0 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More