Review for NeurIPS paper: Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs

Weaknesses: I take issues with two aspects of this submission that lead me to recommend rejection at this point. The submission points out that the evaluations of z(t) at the Chebyshev grid points can be obtained without additional cost, e.g., at line 108. While this is true in some sense in general, there are many numerical theory aspects to this claim that are ignored here, both in the text as well as in the code. Runge-Kutta methods only guarantee high-order approximations at their own grid points. If high-order approximations are sought at pre-defined grid points, there are two solutions: a) the solvers are forced to include the pre-defined grid points as part of the otherwise adaptive mesh or b) a particular choice has to be made to find a smooth-interpolant Runge-Kutta formula.