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

Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs