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Scalable Gradients for Stochastic Differential Equations


Jan 09 2020 David Duvenaud

Training Neural SDEs: We worked out how to do scalable reverse-mode autodiff for stochastic differential equations. This lets us fit SDEs defined by neural nets with black-box adaptive higher-order solvers. With @lxuechen, @rtqichen and @wongtkleonard.
6 replies, 856 likes

Jan 09 2020 Daisuke Okanohara

The gradient of the solutions of stochastic differential equations (SDEs) can be efficiently computed by a stochastic adjoint sensitivity method as NeuralODE, which requires constant-memory and scalable vector-Jacobian product only.
0 replies, 15 likes

Jan 09 2020 Paul Portesi ن​
0 replies, 2 likes

Jan 07 2020 Piotr Sokol

Scalable Gradients for Stochastic Differential Equations. (arXiv:2001.01328v1 [cs.LG])
0 replies, 1 likes