Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations

This paper investigates the approximation properties of deep neural networks with piecewise-polynomial activation functions. We derive the required depth, width, and sparsity of a deep neural network to approximate any Hölder smooth function up to a given approximation error in Hölder norms in such...

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Vydáno v:	Neural networks Ročník 161; s. 242 - 253
Hlavní autoři:	Belomestny, Denis, Naumov, Alexey, Puchkin, Nikita, Samsonov, Sergey
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.04.2023
Témata:	Algorithms Approximation complexity Deep neural networks Hölder class Machine Learning Neural Networks, Computer ReLUk activations ReQU activations Approximation complexity Deep neural networks Hölder class ReQU activations ReLUk activations ReLU(k) activations
ISSN:	0893-6080, 1879-2782, 1879-2782
On-line přístup:	Získat plný text
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Popis
Shrnutí:	This paper investigates the approximation properties of deep neural networks with piecewise-polynomial activation functions. We derive the required depth, width, and sparsity of a deep neural network to approximate any Hölder smooth function up to a given approximation error in Hölder norms in such a way that all weights of this neural network are bounded by 1. The latter feature is essential to control generalization errors in many statistical and machine learning applications. •Rates and complexity for smooth function approximation in Hölder norms by ReQU neural networks.•Explicit and uniform bounds for weights of the approximating neural network.•Exponential convergence rates for analytic functions.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0893-6080 1879-2782 1879-2782
DOI:	10.1016/j.neunet.2023.01.035