3 papers
math.NA2026
Fourier Residual Networks Achieve Spectral Accuracy for Discontinuous Functions
Owen Davis, Mohammad Motamed, Olof Runborg
We present a constructive approximation framework for analyzing the expressive power of Fourier residual networks in approximating a broad class of one-dimensional functions. Our s…
stat.ML2026
Approximation Error and Complexity Bounds for ReLU Networks on Low-Regular Function Spaces
Owen Davis, Gianluca Geraci, Mohammad Motamed
In this work, we consider the approximation of a large class of bounded functions, with minimal regularity assumptions, by ReLU neural networks. We show that the approximation erro…
cs.LG2025
Deep Learning without Global Optimization by Random Fourier Neural Networks
Owen Davis, Gianluca Geraci, Mohammad Motamed
We introduce a new training algorithm for deep neural networks that utilize random complex exponential activation functions. Our approach employs a Markov Chain Monte Carlo samplin…