123 citations · 129 across the 2 of their papers we have counts for
9 papers · 1 filter
On Neural Differential Equations
Patrick Kidger
The conjoining of dynamical systems and deep learning has become a topic of great interest. In particular, neural differential equations (NDEs) demonstrate that neural networks and…
Efficient and Accurate Gradients for Neural SDEs
Patrick Kidger, James Foster, Xuechen Li +1
Neural SDEs combine many of the best qualities of both RNNs and SDEs: memory efficient training, high-capacity function approximation, and strong priors on model space. This makes…
Neural SDEs as Infinite-Dimensional GANs
Patrick Kidger, James Foster, Xuechen Li +2
Stochastic differential equations (SDEs) are a staple of mathematical modelling of temporal dynamics. However, a fundamental limitation has been that such models have typically bee…
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…