1.
Neural Netw
; 161: 242-253, 2023 Apr.
Artigo
em Inglês
| MEDLINE
| ID: mdl-36774863
RESUMO
This paper investigates the approximation properties of deep neural networks with piecewise-polynomial activation functions. We derive the required depth, width, and sparsity of a deep neural network to approximate any Hölder smooth function up to a given approximation error in Hölder norms in such a way that all weights of this neural network are bounded by 1. The latter feature is essential to control generalization errors in many statistical and machine learning applications.