Your comment reminded me this talk [1] by David Duvenaud where he discusses his work on Neural ODEs, introduced in the paper [2] titled "Neural Ordinary Differe

Your comment reminded me this talk [1] by David Duvenaud where he discusses his work on Neural ODEs, introduced in the paper [2] titled "Neural Ordinary Differential Equations" (this was one of the Best Papers in Neurips 2018). Apparently the original name of the paper was "Training Infinitesimally-Layered Neural Networks by Backpropagating through Black-box ODE Solvers". I found that to be much better description for the paper. It seems he thinks so too, but eventually the catch-phrase-y title won out. In the video [1], this bit is at 00:48-01:22. PS: [1] is a good talk in general where he discusses some of the limitations of the paper and things that could have been done better. [1] https://www.youtube.com/watch?v=YZ-_E7A3V2w https://www.youtube.com/watch?v=YZ-_E7A3V2w [2] https://papers.nips.cc/paper_files/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper_files/paper/2018/file/69386f6bb...