Neural Ordinary Differential Equations
Senior author here, I&#x27;m happy to answer any questions.<p>We just released source code:
<a href="https:&#x2F;&#x2F;github.com&#x2F;rtqichen&#x2F;torchdiffeq" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;rtqichen&#x2F;torchdiffeq</a> .
This includes PyTorch implementations of adaptive ODE solvers that can be differentiated through automatically.  So you can mix and match these ODE solvers with any other differentiable model component.<p>There&#x27;s already been a bit of follow-up work, turning Continuous Normalizing Flows into a practical generative density model:
<a href="https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;1810.01367" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;1810.01367</a><p>And now we&#x27;re mainly working on 1) Regularizing ODE nets to be faster to solve and 2) getting the time-series model to scale up and extend it to stochastic differential equations.