155 citations · 207 across the 6 of their papers we have counts for
6 papers
New directions in the applications of rough path theory
Adeline Fermanian, Terry Lyons, James Morrill +1
This article provides a concise overview of some of the recent advances in the application of rough path theory to machine learning. Controlled differential equations (CDEs) are di…
Neural Controlled Differential Equations for Online Prediction Tasks
James Morrill, Patrick Kidger, Lingyi Yang +1
Neural controlled differential equations (Neural CDEs) are a continuous-time extension of recurrent neural networks (RNNs), achieving state-of-the-art (SOTA) performance at modelli…
Neural Rough Differential Equations for Long Time Series
James Morrill, Cristopher Salvi, Patrick Kidger +2
Neural controlled differential equations (CDEs) are the continuous-time analogue of recurrent neural networks, as Neural ODEs are to residual networks, and offer a memory-efficient…
A Generalised Signature Method for Multivariate Time Series Feature Extraction
James Morrill, Adeline Fermanian, Patrick Kidger +1
The 'signature method' refers to a collection of feature extraction techniques for multivariate time series, derived from the theory of controlled differential equations. There is…
Generalised Interpretable Shapelets for Irregular Time Series
Patrick Kidger, James Morrill, Terry Lyons
The shapelet transform is a form of feature extraction for time series, in which a time series is described by its similarity to each of a collection of `shapelets'. However it has…
Neural Controlled Differential Equations for Irregular Time Series
Patrick Kidger, James Morrill, James Foster +1
Neural ordinary differential equations are an attractive option for modelling temporal dynamics. However, a fundamental issue is that the solution to an ordinary differential equat…