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We prove Carl's type inequalities for the error of approximation of compactsets K by deep and shallow neural networks. This in turn gives lower bounds onhow well we can approximate the functions in K when requiring the approximantsto come from outputs of such networks. Our results are obtained as a byproductof the study of the recently introduced Lipschitz widths.

Limitations on approximation by deep and shallow neural networks