Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality in Approximation on Hölder Class-Reference-Cited by-同舟云学术

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Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality in Approximation on Hölder Class

Published:2023-08-18 Issue:4 Volume:55 Page:3635-3649
ISSN:0036-1410
Container-title:SIAM Journal on Mathematical Analysis
language:en
Short-container-title:SIAM J. Math. Anal.

Author:

Jiao Yuling¹,Lai Yanming²,Lu Xiliang¹,Wang Fengru²,Yang Jerry Zhijian¹^ORCID,Yang Yuanyuan²

Affiliation:

1. School of Mathematics and Statistics, and Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan, 430072, People’s Republic of China.

2. School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, People’s Republic of China.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Computational Mathematics,Analysis

Reference34 articles.

1. Universal approximation bounds for superpositions of a sigmoidal function

2. Representation Learning: A Review and New Perspectives

3. Deep Nets for Local Manifold Learning

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1. Analysis of the rate of convergence of two regression estimates defined by neural features which are easy to implement;Electronic Journal of Statistics;2024-01-01

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