Supplementary material

Appendix B proves universal approximation of the Neural CDE model, and is substantially more technical than the rest of this paper. Appendix C proves that the Neural CDE model subsumes alternative ODE models which depend directly and nonlinearly on the data. Appendix D gives the full details of every experiment, such as choice of optimiser, hyperparameter searches, and so on. To evaluate the model as discussed in Section 3.2, X must be at least continuous and piecewise differentiable. A.1 Differentiating with respect to the time points However, there is a technical caveat in the specific case that derivatives with respect to the initial time t A.2 Adaptive step size solvers There is one further caveat that must be considered.