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Probability Seminars

Rough Paths and more scalable data science

With Terry Lyons (Oxford)

Rough Paths and more scalable data science

The basic principle of Rough Path Theory, is that one can understand multi modal and oscillatory streams of data by considering their impact on non-linear controlled dynamical systems.
The approach leads to the local description of these complex streams through signatures; connecting data science with tensor algebra.
These high order signatures of the data are expensive to compute but can be re-used a huge number of times in the training of a neural net without the need to re-compute the signature from the data.

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