Deep ReLU neural networks in high-dimensional approximation

We study the computation complexity of deep ReLU (Rectified Linear Unit) neural networks for the approximation of functions from the Hölder–Zygmund space of mixed smoothness defined on the d-dimensional unit cube when the dimension d may be very large. The approximation error is measured in the norm...

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Vydáno v:	Neural networks Ročník 142; s. 619 - 635
Hlavní autoři:	Dũng, Dinh, Nguyen, Van Kien
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.10.2021
Témata:	Computation complexity Continuous piece-wise linear functions Deep ReLU neural network High-dimensional approximation Hölder–Zygmund space of mixed smoothness Sparse-grid sampling High-dimensional approximation Computation complexity Deep ReLU neural network Hölder–Zygmund space of mixed smoothness Sparse-grid sampling Continuous piece-wise linear functions
ISSN:	0893-6080, 1879-2782, 1879-2782
On-line přístup:	Získat plný text
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