Smoothly activated DNNs (feedforward and residual) achieve non-asymptotic uniform convergence rates that mitigate the curse of dimensionality by adaptively using hierarchical composition structure of the target function.
arXiv preprint arXiv:2511.08772 , year=
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Mitigating the Curse of Dimensionality in Uniform Convergence of Deep Neural Networks via Smooth Activations
Smoothly activated DNNs (feedforward and residual) achieve non-asymptotic uniform convergence rates that mitigate the curse of dimensionality by adaptively using hierarchical composition structure of the target function.