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Approximation rates for neural networks with general activation functions.
- Source :
-
Neural Networks . Aug2020, Vol. 128, p313-321. 9p. - Publication Year :
- 2020
-
Abstract
- We prove some new results concerning the approximation rate of neural networks with general activation functions. Our first result concerns the rate of approximation of a two layer neural network with a polynomially-decaying non-sigmoidal activation function. We extend the dimension independent approximation rates previously obtained to this new class of activation functions. Our second result gives a weaker, but still dimension independent, approximation rate for a larger class of activation functions, removing the polynomial decay assumption. This result applies to any bounded, integrable activation function. Finally, we show that a stratified sampling approach can be used to improve the approximation rate for polynomially decaying activation functions under mild additional assumptions. [ABSTRACT FROM AUTHOR]
- Subjects :
- *INTEGRABLE functions
*APPROXIMATION theory
*RATES
Subjects
Details
- Language :
- English
- ISSN :
- 08936080
- Volume :
- 128
- Database :
- Academic Search Index
- Journal :
- Neural Networks
- Publication Type :
- Academic Journal
- Accession number :
- 143683113
- Full Text :
- https://doi.org/10.1016/j.neunet.2020.05.019