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Scalable Gradients for Stochastic Differential Equations

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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. https://arxiv.org/pdf/2001.01328.pdf With @lxuechen, @rtqichen and @wongtkleonard. https://t.co/qlUwMxezjO

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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. https://arxiv.org/abs/2001.01328

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Paul Portesi ن​: https://twitter.com/DavidDuvenaud/status/1215347970159382534?s=20

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Piotr Sokol: Scalable Gradients for Stochastic Differential Equations. (arXiv:2001.01328v1 [cs.LG]) http://arxiv.org/abs/2001.01328

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注目の最新arXiv【毎日更新】: 2020/01/05 投稿 1位 LG(Machine Learning) Scalable Gradients for Stochastic Differential Equations https://arxiv.org/abs/2001.01328 7 Tweets 24 Retweets 137 Favorites

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Found on Jan 09 2020 at https://arxiv.org/pdf/2001.01328.pdf

PDF content of a computer science paper: Scalable Gradients for Stochastic Differential Equations