Diego Mondéjar

Resolution-Consistent Greedy Neural Approximation on Infinite-Dimensional Spaces

This work develops constructive approximation and learning guarantees for shallow neural models with infinite-dimensional inputs observed through finitely many coordinates and extends to Hilbert-valued responses without an explicit dependence on the output dimension.