On the capacity of deep generative networks for approximating distributions

We study the efficacy and efficiency of deep generative networks for approximating probability distributions. We prove that neural networks can transform a low-dimensional source distribution to a distribution that is arbitrarily close to a high-dimensional target distribution, when the closeness is...

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Veröffentlicht in:	Neural networks Jg. 145; S. 144 - 154
Hauptverfasser:	Yang, Yunfei, Li, Zhen, Wang, Yang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.01.2022
Schlagworte:	Approximation complexity Benchmarking Deep ReLU networks Generative adversarial networks Maximum mean discrepancy Neural Networks, Computer Probability Wasserstein distance Generative adversarial networks Approximation complexity Wasserstein distance Deep ReLU networks Maximum mean discrepancy
ISSN:	0893-6080, 1879-2782, 1879-2782
Online-Zugang:	Volltext
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