  We prove a theorem concerning the approximation of multivariate functions by
deep ReLU networks, for which the curse of the dimensionality is lessened. Our
theorem is based on a constructive proof of the Kolmogorov--Arnold
superposition theorem, and on a subset of multivariate continuous functions
whose outer superposition functions can be efficiently approximated by deep
ReLU networks.
