  We propose a simple interpolation-based method for the efficient
approximation of gradients in neural ODE models. We compare it with the reverse
dynamic method (known in the literature as "adjoint method") to train neural
ODEs on classification, density estimation, and inference approximation tasks.
We also propose a theoretical justification of our approach using logarithmic
norm formalism. As a result, our method allows faster model training than the
reverse dynamic method that was confirmed and validated by extensive numerical
experiments for several standard benchmarks.
