Analytic function approximation by path norm regularized deep networks

We show that neural networks with absolute value activation function and with the path norm, the depth, the width and the network weights having logarithmic dependence on $1\mathrm{/}\epsilon$ can $\epsilon$-approximate functions that are analytic on certain regions of ${\mathbb{C}}^d$.