, I tend to vote in favor of this paper. * Detailed remarks: - The analysis in Figure 4 is very interesting. What is a possible explanation for the behaviour in Figure 4(d), showing that the number of function evaluations automatically increases with the epochs? Consequently, how is it possible to control the tradeoff between accuracy and computation time if, automatically, the computation time increases along the training? In this direction, I think it would be nice to see how classification accuracy evolves (e.g. on MNIST) with the precision required. - In Figure 6, an interpolation experiment shows that the probability distribution evolves smoothly along time, which is an indirect way to interpret it. Since this is a low (2D) dimensional case, wouldn't it be possible to directly analyze the learnt ODE function, by looking at its fixed points and their stability? - For the continuous-time time-series model, subsec 6.1 clarity should be improved. Regarding the autoencoder formulation, why is an RNN used for the encoder, and not an ODE-like layer? Indeed, the authors argue that RNNs have trouble coping with such time-series, so this might also be the case in the encoding part. - Do the authors plan to share the code of the experiments (not only of the main module)? - I think it would be better if notations in appendix A followed the notations of the main paper. - Section 3 and Section 4 are slightly redundant: maybe putting the first paragraph of sec 4 in sec 3 and putting the remainder of sec 4 before section 3 would help.
- Section 3 and Section 4 are slightly redundant: maybe putting the first paragraph of sec 4 in sec 3 and putting the remainder of sec 4 before section 3 would help.