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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Veröffentlicht in:	Neural networks Jg. 142; S. 619 - 635
Hauptverfasser:	Dũng, Dinh, Nguyen, Van Kien
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.10.2021
Schlagworte:	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
Online-Zugang:	Volltext
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