Abstract

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.

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