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Log Signatures and Neural Controlled Differential Equations

Presented by: 
James Morrill University of Oxford
Tuesday 16th March 2021 - 14:00 to 14:25
INI Seminar Room 1
Session Title: 
Log Signatures and Controlled Differential Equations
Session Chair: 
Harald Oberhauser
Neural Ordinary Differential Equations are the continuous time extension of residual networks. They combine two dominant modelling paradigms in Neural Networks and Differential Equations and result in a host of benefits over a standard neural net. However, being ODEs, their solution trajectory is uniquely defined by the initial state of the system. In the case of sequential data (e.g. a time series), it is imperative that the solution trajectory can be updated based on incoming data. In this talk we describe the Neural Controlled Differential Equation - which can be thought as ODE extension to an RNN - and depends continuously on the incoming data. We give experiments that demonstrate state-of-the-art performance across a range of modelling tasks, and go on to describe the application areas the model is expected to be of most utility.
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons