Michael Feischl

High Probability Derivative Bounds for Random tanh Neural Networks on a Hypercube

High-probability bounds for mixed input derivatives of wide random neural networks whose activation derivatives satisfy a factorial growth bound are established and the derivative estimates are connected to quasi-Monte Carlo integration and indicate how such regularity can enter the analysis of QMC-based training.