Approximation of smooth functionals using deep ReLU networks.

Approximation theory Deep learning theory Fréchet derivative Polynomial rates ReLU Smooth functionals

Journal

Neural networks : the official journal of the International Neural Network Society
ISSN: 1879-2782
Titre abrégé: Neural Netw
Pays: United States
ID NLM: 8805018

Informations de publication

Date de publication:
Sep 2023
Historique:
received: 20 07 2022
revised: 11 04 2023
accepted: 11 07 2023
medline: 11 9 2023
pubmed: 8 8 2023
entrez: 7 8 2023
Statut: ppublish

Résumé

In recent years, deep neural networks have been employed to approximate nonlinear continuous functionals F defined on L

Identifiants

pubmed: 37549610
pii: S0893-6080(23)00371-4
doi: 10.1016/j.neunet.2023.07.012
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

424-436

Informations de copyright

Copyright © 2023 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Linhao Song (L)

School of Mathematical Science, Beihang University, Beijing, China; School of Data Science, City University of Hong Kong, Kowloon, Hong Kong. Electronic address: linhasong2-c@my.cityu.edu.hk.

Ying Liu (Y)

Laboratory for AI-Powered Financial Technologies, Hong Kong Science Park, Shatin, New Territories, Hong Kong. Electronic address: yingliu@hkaift.com.

Jun Fan (J)

Department of Mathematics, Hong Kong Baptist University, Kowloon, Hong Kong. Electronic address: junfan@hkbu.edu.hk.

Ding-Xuan Zhou (DX)

School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia. Electronic address: dingxuan.zhou@sydney.edu.au.

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