292 citations · 760 across the 55 of their papers we have counts for
59 papers
Learning to Learn Financial Networks for Optimising Momentum Strategies
Xingyue Pu, Stefan Zohren, Stephen Roberts +1
Network momentum provides a novel type of risk premium, which exploits the interconnections among assets in a financial network to predict future returns. However, the current proc…
The instabilities of large learning rate training: a loss landscape view
Lawrence Wang, Stephen Roberts
Modern neural networks are undeniably successful. Numerous works study how the curvature of loss landscapes can affect the quality of solutions. In this work we study the loss land…
Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies
Tom Liu, Stephen Roberts, Stefan Zohren
We introduce Deep Inception Networks (DINs), a family of Deep Learning models that provide a general framework for end-to-end systematic trading strategies. DINs extract time serie…
G-TRACER: Expected Sharpness Optimization
John Williams, Stephen Roberts
We propose a new regularization scheme for the optimization of deep learning architectures, G-TRACER ("Geometric TRACE Ratio"), which promotes generalization by seeking flat minima…
SANE: The phases of gradient descent through Sharpness Adjusted Number of Effective parameters
Lawrence Wang, Stephen J. Roberts
Modern neural networks are undeniably successful. Numerous studies have investigated how the curvature of loss landscapes can affect the quality of solutions. In this work we consi…
On Sequential Bayesian Inference for Continual Learning
Samuel Kessler, Adam Cobb, Tim G. J. Rudner +2
Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks. We revis…