# Towards Understanding Normalization in Neural ODEs

arXiv: https://arxiv.org/abs/2004.09222 · HTML: https://mlsanity.com/paper/2004.09222/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [Momentum Residual Neural Networks](https://mlsanity.com/paper/2102.07870/) (2021)
- [On Numerical Integration in Neural Ordinary Differential Equations](https://mlsanity.com/paper/2206.07335/) (2022)
- [Stateful ODE-Nets using Basis Function Expansions](https://mlsanity.com/paper/2106.10820/) (2021)
- [Stability-Informed Initialization of Neural Ordinary Differential Equations](https://mlsanity.com/paper/2311.15890/) (2023)
- [Meta-Solver for Neural Ordinary Differential Equations](https://mlsanity.com/paper/2103.08561/) (2021)
- [Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting](https://mlsanity.com/paper/2210.04958/) (2022)
- [Time-vectorized numerical integration for systems of ODEs](https://mlsanity.com/paper/2310.08649/) (2023)

## Abstract (arXiv)

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.