# Augmented Neural ODEs

arXiv: https://arxiv.org/abs/1904.01681 · HTML: https://mlsanity.com/paper/1904.01681/

## Key facts

- Cited by 397 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 30 of 397)

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## Abstract (arXiv)

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.