2 citations · 5 across the 7 of their papers we have counts for
Showing 2021Show all
2 papers · 1 filter
cs.LG2021★ 2 cited
Conditionally Gaussian PAC-Bayes
Eugenio Clerico, George Deligiannidis, Arnaud Doucet
Recent studies have empirically investigated different methods to train stochastic neural networks on a classification task by optimising a PAC-Bayesian bound via stochastic gradie…
stat.ML2021★ 2 cited
Wide stochastic networks: Gaussian limit and PAC-Bayesian training
Eugenio Clerico, George Deligiannidis, Arnaud Doucet
The limit of infinite width allows for substantial simplifications in the analytical study of over-parameterised neural networks. With a suitable random initialisation, an extremel…