There must be more than a few people here who have read Neural Ordinary Differential Equations ( https://arxiv.org/pdf/1806.07366.pdf ), and while I understand the general concept of this, there are some points that are quite unclear to me.

1. What exactly does the adjoint state (and the augmented adjoint state) represent?
2. In section 5 (generative latent function time-series model), how is the gradient f guaranteed to be invariant to time?

I’ve been looking and searching for more papers, previous works, videos, posts, etc for more insight, and some have helped me a lot, but still got questions coming up endlessly to completely understand this paper. I think the idea of using an ODE solver to model a ‘continuous’ network is quite interesting, though. I wanted to post to see if you guys had more insight into this paper.