Log Signatures and Neural Controlled Differential Equations

Add to your list(s) Download to your calendar using vCal

• James Morrill (University of Oxford)
• Tuesday 16 March 2021, 14:00-14:25
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact INI IT.

This talk has been canceled/deleted

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.

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

This talk is not included in any other list

Note that ex-directory lists are not shown.