Neural SDEs Made Easy: SDEs are Infinite-Dimensional GANs
Several authors have introduced \emph{Neural Stochastic Differential Equations} (Neural SDEs), often involving complex theory with various limitations. Here, we aim to introduce a generic, user friendly approach to neural SDEs. Our central contribution is the observation that an SDE is a map from Wiener measure (Brownian motion) to a solution distribution, which may be sampled from, but which does not admit a straightforward notion of probability density -- and that this is just the familiar formulation of a GAN. This produces a continuous-time generative model, arbitrary drift and diffusions are admissible, and in the infinite data limit any SDE may be learnt. After that, we construct a new scheme for sampling \emph{and reconstructing} Brownian motion, with constant average-case time and memory costs, adapted to the access patterns of an SDE solver. Finally, we demonstrate that the adjoint SDE (used for backpropagation) may be constructed via rough path theory, without the previous theoretical complexity of two-sided filtrations.
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