Deep Network Approximation Characterized by Number of Neurons

This paper quantitatively characterizes the approximation power of deep feed-forward neural networks (FNNs) in terms of the number of neurons. It is shown by construction that ReLU FNNs with width $\mathcal{O}\big(\max\{d\lfloor N^{1/d}\rfloor,\, N+1\}\big)$ and depth $\mathcal{O}(L)$ can approximate an arbitrary Hölder continuous function of order $α\in (0,1]$ on $[0,1]^d$ with a nearly tight approximation rate $\mathcal{O}\big(\sqrt{d} N^{-2α/d}L^{-2α/d}\big)$ measured in $L^p$-norm for any $N,L\in \mathbb{N}^+$ and $p\in[1,\infty]$. More generally for an arbitrary continuous function $f$ on $[0,1]^d$ with a modulus of continuity $ω_f(\cdot)$, the constructive approximation rate is $\mathcal{O}\big(\sqrt{d}\,ω_f( N^{-2/d}L^{-2/d})\big)$. We also extend our analysis to $f$ on irregular domains or those localized in an $\varepsilon$-neighborhood of a $d_{\mathcal{M}}$-dimensional smooth manifold $\mathcal{M}\subseteq [0,1]^d$ with $d_{\mathcal{M}}\ll d$. Especially, in the case of an essentially low-dimensional domain, we show an approximation rate $\mathcal{O}\big(ω_f(\tfrac{\varepsilon}{1-δ}\sqrt{\tfrac{d}{d_δ}}+\varepsilon)+\sqrt{d}\,ω_f(\tfrac{\sqrt{d}}{(1-δ)\sqrt{d_δ}}N^{-2/d_δ}L^{-2/d_δ})\big)$ for ReLU FNNs to approximate $f$ in the $\varepsilon$-neighborhood, where $d_δ=\mathcal{O}\big(d_{\mathcal{M}}\tfrac{\ln (d/δ)}{δ^2}\big)$ for any $δ\in(0,1)$ as a relative error for a projection to approximate an isometry when projecting $\mathcal{M}$ to a $d_δ$-dimensional domain.