51 citations · 124 across the 23 of their papers we have counts for
5 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…
Semi-unsupervised Learning of Human Activity using Deep Generative Models
Matthew Willetts, Aiden Doherty, Stephen Roberts +1
We introduce 'semi-unsupervised learning', a problem regime related to transfer learning and zero-shot learning where, in the training data, some classes are sparsely labelled and…
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…
MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting
Bernardo Pérez Orozco, Gabriele Abbati, Stephen Roberts
Time series forecasting is ubiquitous in the modern world. Applications range from health care to astronomy, and include climate modelling, financial trading and monitoring of crit…