论文标题
要理解神经氧的归一化
Towards Understanding Normalization in Neural ODEs
论文作者
论文摘要
归一化是深度学习中的一项重要且经过广泛研究的技术。但是，它在普通微分方程基于基于微分方程的网络（神经ODE）方面的作用仍然很少了解。本文研究了不同的归一化技术如何影响神经ODE的性能。特别是，我们表明可以在CIFAR-10分类任务中实现93％的准确性，据我们所知，这是在此问题上测试的神经ODE中报告的最高准确性。
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.
