arXiv stat.ML
· Papers
Weighted universal approximation of differentiable maps on infinite-dimensional manifolds
arXiv:2606.09820v2 Announce Type: replace-cross Abstract: We generalize the universal approximation theorem for functional input neural networks (FNN) to differentiable maps by including the approximation of the derivatives. A FNN maps the input from a possibly infinite-dimensional weighted manifold to the real-valued