no code implementations • 20 Dec 2023 • Eugenio Clerico, Benjamin Guedj
We establish explicit dynamics for neural networks whose training objective has a regularising term that constrains the parameters to remain close to their initial value.
no code implementations • 6 Sep 2022 • Eugenio Clerico, Tyler Farghly, George Deligiannidis, Benjamin Guedj, Arnaud Doucet
We establish disintegrated PAC-Bayesian generalisation bounds for models trained with gradient descent methods or continuous gradient flows.
no code implementations • 2 Mar 2022 • Eugenio Clerico, Amitis Shidani, George Deligiannidis, Arnaud Doucet
This work discusses how to derive upper bounds for the expected generalisation error of supervised learning algorithms by means of the chaining technique.
1 code implementation • 22 Oct 2021 • 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 gradient descent.
1 code implementation • 17 Jun 2021 • Eugenio Clerico, George Deligiannidis, Arnaud Doucet
The limit of infinite width allows for substantial simplifications in the analytical study of over-parameterised neural networks.
no code implementations • 24 Oct 2020 • Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet, Judith Rousseau
Deep ResNet architectures have achieved state of the art performance on many tasks.
