Senior author here, I'm happy to answer any questions.
We just released source code:
https://github.com/rtqichen/torchdiffeq .
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.
There's already been a bit of follow-up work, turning Continuous Normalizing Flows into a practical generative density model:
https://arxiv.org/abs/1810.01367
And now we'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.
We just released source code: https://github.com/rtqichen/torchdiffeq . 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.
There's already been a bit of follow-up work, turning Continuous Normalizing Flows into a practical generative density model: https://arxiv.org/abs/1810.01367
And now we'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.