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Near-optimal deep neural network approximation for Korobov functions with respect to Lp and H1 norms

Published:2024-12 Issue: Volume:180 Page:106702
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Yang Yahong^ORCID,Lu Yulong^ORCID

Funder

National Science Foundation

Publisher

Elsevier BV

Reference57 articles.

1. The Vapnik-Chervonenkis dimension: Information versus complexity in learning;Abu-Mostafa;Neural Computation,1989

2. Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks;Adcock,2024

3. Understanding deep neural networks with rectified linear units;Arora,2016

4. Universal approximation bounds for superpositions of a sigmoidal function;Barron;IEEE Transactions on Information Theory,1993

5. Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks;Bartlett;Journal of Machine Learning Research,2019

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