REML

Gradient-Flow Optimization as Dynamic Random-Effects Inference: Testing and Early Stopping with Applications to Deep Learning

arXiv:2605.27991v2 Announce Type: replace-cross Abstract: Gradient-flow optimization is usually viewed as an algorithmic procedure for minimizing empirical loss, with training duration selected by validation or heuristic early-stopping rules. We develop a statistical inference framework for the gradient-flow training trajectory itself. The central object is fixed-operator squared-error gradient flow: whenever the fitted value evolves through a time-invariant positive semidefinite training...