150 citations · 416 across the 41 of their papers we have counts for
4 papers · 1 filter
BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books
Zihao Zhang, Stefan Zohren, Stephen Roberts
We showcase how dropout variational inference can be applied to a large-scale deep learning model that predicts price movements from limit order books (LOBs), the canonical data so…
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…