  We show that Neural Ordinary Differential Equations (ODEs) learn
representations that preserve the topology of the input space and prove that
this implies the existence of functions Neural ODEs cannot represent. To
address these limitations, we introduce Augmented Neural ODEs which, in
addition to being more expressive models, are empirically more stable,
generalize better and have a lower computational cost than Neural ODEs.
