  The universal approximation property of width-bounded networks has been
studied as a dual of classical universal approximation results on depth-bounded
networks. However, the critical width enabling the universal approximation has
not been exactly characterized in terms of the input dimension $d_x$ and the
output dimension $d_y$. In this work, we provide the first definitive result in
this direction for networks using the ReLU activation functions: The minimum
width required for the universal approximation of the $L^p$ functions is
exactly $\max\{d_x+1,d_y\}$. We also prove that the same conclusion does not
hold for the uniform approximation with ReLU, but does hold with an additional
threshold activation function. Our proof technique can be also used to derive a
tighter upper bound on the minimum width required for the universal
approximation using networks with general activation functions.

Machine Learning;Machine Learning