We give analytical results for propagation of uncertainty through trained multi-layer perceptrons (MLPs) with a single hidden layer and ReLU activation functions. More precisely, we give expressions for the mean and variance of the output when the input is multivariate Gaussian. In contrast to previous results, we obtain exact expressions without resort to a series expansion.

翻译：本文针对经过训练的单隐藏层多层感知机（MLPs）在采用ReLU激活函数时的不确定性传播问题，给出了解析结果。具体而言，当输入为多元高斯分布时，我们推导出了输出均值和方差的表达式。与以往研究不同，我们无需借助级数展开即可获得精确的解析表达式。