21 citations · 34 across the 5 of their papers we have counts for
5 papers
Extending Deep Learning Models for Limit Order Books to Quantile Regression
Zihao Zhang, Stefan Zohren, Stephen Roberts
We showcase how Quantile Regression (QR) can be applied to forecast financial returns using Limit Order Books (LOBs), the canonical data source of high-frequency financial time-ser…
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning
Diego Granziol, Binxin Ru, Stefan Zohren +3
Efficient approximation lies at the heart of large-scale machine learning problems. In this paper, we propose a novel, robust maximum entropy algorithm, which is capable of dealing…
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs
Bryan Lim, Stefan Zohren, Stephen Roberts
Despite recent innovations in network architectures and loss functions, training RNNs to learn long-term dependencies remains difficult due to challenges with gradient-based optimi…
Entropic Determinants
Diego Granziol, Stephen Roberts
The ability of many powerful machine learning algorithms to deal with large data sets without compromise is often hampered by computationally expensive linear algebra tasks, of whi…
An information and field theoretic approach to the grand canonical ensemble
Diego Granziol, Stephen Roberts
We present a novel derivation of the constraints required to obtain the underlying principles of statistical mechanics using a maximum entropy framework. We derive the mean value c…