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Rough Paths and more scalable data science

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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.

This talk is part of the Probability series.

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