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Approximation theory is concerned with how functions can best be approximated with simpler functions, and with quantitatively characterizing the errors introduced thereby.

1 vote

Invertibility of neural network as operator on Wasserstein space

This is just a partial answer regarding $S$ being injective. The generalisation of your argument is given by Hornik (Theorem 5 and the definition of discriminatory functions above Theorem 5)
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2 votes
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Uniform Lipschitz function approximation by shallow neural networks

Fix $d\in \mathbb{N}$. Let $F_1$ be the set of all 1-Lipschitz functions mapping $[0, 1]^d$ to $\mathbb{R}$. For $\varphi: \mathbb{R} \rightarrow \mathbb{R}$ and $m \in \mathbb{N}$, let $N_\varphi^m$ …
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