An upper bound on the variance of scalar multilayer perceptrons for log-concave distributions

摘要

In this paper, we give an upper bound on the variance of scalar multilayer perceptrons. The distribution of the input is assumed to be the class of log-concave distributions, which includes the well-known Gaussian distribution. The activation functions of the scalar multilayer perceptrons are assumed to be differentiable and Lipschitz continuous.