2022 - T3 - WS1 - Non-Linear and High Dimensional Inference

Collection 2022 - T3 - WS1 - Non-Linear and High Dimensional Inference

Organisateur(s) Aamari, Eddie ; Aaron, Catherine ; Chazal, Frédéric ; Fischer, Aurélie ; Hoffmann, Marc ; Le Brigant, Alice ; Levrard, Clément ; Michel, Bertrand

Date(s) 03/10/2022 - 07/10/2022

URL associée https://indico.math.cnrs.fr/event/7545/

2 21

Overcoming the curse of dimensionality with deep neural networks

De Sophie Langer

Although the application of deep neural networks to real-world problems has become ubiquitous, the question of why they are so effective has not yet been satisfactorily answered. However, some progress has been made in establishing an underlying mathematical foundation. This talk surveys results on statistical risk bounds of deep neural networks. In particular, we focus on the question of when neural networks bypass the curse of dimensionality. Here we discuss results for vanilla feedforward and convolutional neural networks as well as regression and classification settings.

Informations sur la vidéo

Date de captation 03/10/2022
Date de publication 03/05/2024
Institut IHP
Langue Anglais
Audience Chercheurs, Doctorants, Etudiants
Réalisateur(s) Alexandre Duplessis, Marco Perez
Format MP4
Lieu Amphitheater Hermite, IHP

Données de citation

DOI 10.57987/IHP.2022.T3.WS1.002
Citer cette vidéo Langer, Sophie (03/10/2022). Overcoming the curse of dimensionality with deep neural networks. IHP. Audiovisual resource. DOI: 10.57987/IHP.2022.T3.WS1.002
URL https://dx.doi.org/10.57987/IHP.2022.T3.WS1.002

Domaine(s)

Statistiques - Théorie

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