On dimension-independent approximation by neural networks and linear approximators

Sets of multivariable functions that can be approximated with "dimension-independent" rates either by linear approximators or by neural networks having various types of computational units are compared. The comparison is made by exhibiting families of functions belonging to suitable difference sets.