Yun-Fei Yang

Error bounds in Sobolev norms for approximations with norm constrained ReLU neural networks

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.