67 citations · 147 across the 28 of their papers we have counts for
3 papers · 2 filters
Practical Bayesian Learning of Neural Networks via Adaptive Optimisation Methods
Samuel Kessler, Arnold Salas, Vincent W. C. Tan +2
We introduce a novel framework for the estimation of the posterior distribution over the weights of a neural network, based on a new probabilistic interpretation of adaptive optimi…
Entropic Spectral Learning for Large-Scale Graphs
Diego Granziol, Binxin Ru, Stefan Zohren +3
Graph spectra have been successfully used to classify network types, compute the similarity between graphs, and determine the number of communities in a network. For large graphs,…
Gradient descent in Gaussian random fields as a toy model for high-dimensional optimisation in deep learning
Mariano Chouza, Stephen Roberts, Stefan Zohren
In this paper we model the loss function of high-dimensional optimization problems by a Gaussian random field, or equivalently a Gaussian process. Our aim is to study gradient desc…