AI RESEARCH
Time-Frequency Analysis for Neural Networks
arXiv CS.LG
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ArXi:2512.15992v2 Announce Type: replace-cross We develop a quantitative approximation theory for shallow neural networks using tools from time-frequency analysis. Working in weighted modulation spaces $M^{p,q}_m(\mathbf{R}^{d})$, we prove dimension-independent approximation rates in Sobole norms $W^{n,r}(\Omega)$ for networks whose units combine standard activations with localized time-frequency windows.