Boundary Geometry Determines Optimal Shallow ReLU Approximation under Weighted Coefficient Constraints
arXiv:2610.36854v1 Announce Type: new Abstract: We study how boundary geometry affects $L^q$ approximation by shallow ReLU neural networks on convex bodies $K\subset\mathbb R^d$. The target classes are unit balls of weighted variation spaces that can enlarge classical spaces by incorporating domain…
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- 2026-10-28 04:00 · arXiv stat.ML
Boundary Geometry Determines Optimal Shallow ReLU Approximation under Weighted Coefficient Constraints