Approximation Rates for Neural Networks with Encodable Weights in Smoothness Spaces

We examine the necessary and sufficient complexity of neural networks to approximate functions from different smoothness spaces under the restriction of encodable network weights. Based on an entropy argument, we start by proving lower bounds for the number of nonzero encodable weights for neural ne...

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Bibliographic Details
Published inarXiv.org
Main Authors Gühring, Ingo, Raslan, Mones
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 18.09.2020
Subjects
Approximation
Function space
Lower bounds
Mathematical analysis
Neural networks
Norms
Partial differential equations
Smoothness
Sobolev space
Upper bounds
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Summary:We examine the necessary and sufficient complexity of neural networks to approximate functions from different smoothness spaces under the restriction of encodable network weights. Based on an entropy argument, we start by proving lower bounds for the number of nonzero encodable weights for neural network approximation in Besov spaces, Sobolev spaces and more. These results are valid for all sufficiently smooth activation functions. Afterwards, we provide a unifying framework for the construction of approximate partitions of unity by neural networks with fairly general activation functions. This allows us to approximate localized Taylor polynomials by neural networks and make use of the Bramble-Hilbert Lemma. Based on our framework, we derive almost optimal upper bounds in higher-order Sobolev norms. This work advances the theory of approximating solutions of partial differential equations by neural networks.
ISSN:2331-8422