We study the necessary and sufficient complexity of ReLU neural networks—in terms of depth and number of weights—which is required for approximating classifier functions in an L-sense. As a model class, we consider the set E(R) of possibly discontinuous piecewise C functions f : [−1/2, 1/2] → R, where the different “smooth regions” of f are separated by C… (More)