12 citations · 12 across the 1 of their papers we have counts for
3 papers
Generalization bounds for deep learning
Guillermo Valle-Pérez, Ard A. Louis
Generalization in deep learning has been the topic of much recent theoretical and empirical research. Here we introduce desiderata for techniques that predict generalization errors…
Is SGD a Bayesian sampler? Well, almost
Chris Mingard, Guillermo Valle-Pérez, Joar Skalse +1
Overparameterised deep neural networks (DNNs) are highly expressive and so can, in principle, generate almost any function that fits a training dataset with zero error. The vast ma…
Deep learning generalizes because the parameter-function map is biased towards simple functions
Guillermo Valle-Pérez, Chico Q. Camargo, Ard A. Louis
Deep neural networks (DNNs) generalize remarkably well without explicit regularization even in the strongly over-parametrized regime where classical learning theory would instead p…