  We seek to improve the pooling operation in neural networks, by applying a
more theoretically justified operator. We demonstrate that LogSumExp provides a
natural OR operator for logits. When one corrects for the number of elements
inside the pooling operator, this becomes $\text{LogAvgExp} :=
\log(\text{mean}(\exp(x)))$. By introducing a single temperature parameter,
LogAvgExp smoothly transitions from the max of its operands to the mean (found
at the limiting cases $t \to 0^+$ and $t \to +\infty$). We experimentally
tested LogAvgExp, both with and without a learnable temperature parameter, in a
variety of deep neural network architectures for computer vision.
