[D] About Neural Ordinary Differential Equations
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.
- What exactly does the adjoint state (and the augmented adjoint state) represent?
- 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.