Sparse Random-Feature Neural Networks with Krylov-Based SVD for Singularly Perturbed ODE
arXiv:2605.07286v1 Announce Type: cross
Abstract: Random-feature neural networks (RFNNs), including architectures with fixed hidden layers and analytically determined output weights, offer fast training but often suffer from issues due to dense repres…