Abstract: This talk reviews a theory of the functions learned by ReLU neural networks. At its core is the observation that deep ReLU networks can be characterized as solutions to data-fitting problems in certain infinite dimensional function spaces. The solutions are compositions of functions from Banach spaces of second-order bounded variation, defined in the Radon…