292 citations · 760 across the 55 of their papers we have counts for
9 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…
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…
A General Framework for Fair Regression
Jack Fitzsimons, AbdulRahman Al Ali, Michael Osborne +1
Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness c…
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
Zihao Zhang, Stefan Zohren, Stephen Roberts
We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to captur…
Thresholded ConvNet Ensembles: Neural Networks for Technical Forecasting
Sid Ghoshal, Stephen J. Roberts
Much of modern practice in financial forecasting relies on technicals, an umbrella term for several heuristics applying visual pattern recognition to price charts. Despite its ubiq…