deep random feature networks

Article published in IMA Journal of Numerical Analysis

1 min read · Mon, Oct 24 2022

residual network deep random feature networks supervised learning layer- by-layer algorithm

In September 2022, the IMA Journal of Numerical Analysis published the article Smaller generalization error derived for a deep residual neural network compared with shallow networks, by Aku Kammonen (KAUST), Jonas Kiessling (KTH Royal Institute of Technology), Petr Plecháč (University of Delaware), Mattias Sandberg (KTH Royal Institute of Technology), Anders Szepessy (KTH Royal Institute of Technology), and Raul Tempone (KAUST). Abstract: Estimates of the generalization error are proved for a residual neural network with L random Fourier features layers. An optimal distribution for the