Towards Understanding Normalization in Neural ODEs [article]

Julia Gusak, Larisa Markeeva, Talgat Daulbaev, Alexandr Katrutsa, Andrzej Cichocki, Ivan Oseledets
<span title="2020-04-27">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.09222v2">arXiv:2004.09222v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/q4qoencwjvchzclm2qkvfhjnrq">fatcat:q4qoencwjvchzclm2qkvfhjnrq</a> </span>
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