AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is shown that d-dimensional spectral Barron functions with smoothness index $s>0$ can be approximated by deep ReLU neural networks with approximation rate $\widetilde{\mathcal{O}} (S^{-\frac{1}{2}-\frac{s}{d}})$, and it is proved that this convergence rate is minimax optimal up to logarithmic factors.
This work analyzes how well Sobolev functions in W^{n,p} can be approximated by neural networks with width $W$, depth $L$ and path norm bounded by $K$ when the approximation error is measured in the $W^{1,p}$-norm.
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