  Normalization is an important and vastly investigated technique in deep
learning. However, its role for Ordinary Differential Equation based networks
(neural ODEs) is still poorly understood. This paper investigates how different
normalization techniques affect the performance of neural ODEs. Particularly,
we show that it is possible to achieve 93% accuracy in the CIFAR-10
classification task, and to the best of our knowledge, this is the highest
reported accuracy among neural ODEs tested on this problem.
