Rough Paths and more scalable data science
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If you have a question about this talk, please contact Jason Miller.
The basic principle of Rough Path Theory, is that one can understand multi modal and oscillatory streams of data by considering their impact on nonlinear 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 reused a huge number of times in the training of a neural net without the need to recompute the signature from the data.
This talk is part of the Probability series.
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