Analysis of the rate of convergence of fully connected deep neural network regression estimates with smooth activation function
Abstract
This article contributes to the current statistical theory of deep neural networks (DNNs). It was shown that DNNs are able to circumvent the socalled curse of dimensionality in case that suitable restrictions on the structure of the regression function hold. In most of those results the tuning parameter is the sparsity of the network, which describes the number of nonzero weights in the network. This constraint seemed to be the key factor for the good rate of convergence results. Recently, the assumption was disproved. In particular, it was shown that simple fully connected DNNs can achieve the same rate of convergence. Those fully connected DNNs are based on the unbounded ReLU activation function. In this article we extend the results to smooth activation functions, i.e., to the sigmoid activation function. It is shown that estimators based on fully connected DNNs with sigmoid activation function also achieve the minimax rates of convergence (up to $\ln n$factors). In our result the number of hidden layers is fixed, the number of neurons per layer tends to infinity for sample size tending to infinity and a bound for the weights in the network is given.
 Publication:

arXiv eprints
 Pub Date:
 October 2020
 arXiv:
 arXiv:2010.06168
 Bibcode:
 2020arXiv201006168L
 Keywords:

 Mathematics  Statistics Theory;
 Primary 62G08;
 Secondary 41A25;
 82C32
 EPrint:
 arXiv admin note: substantial text overlap with arXiv:1908.11133