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Papers/Augmented Neural ODEs

Augmented Neural ODEs

Emilien Dupont, Arnaud Doucet, Yee Whye Teh

2019-04-02NeurIPS 2019 12Image Classification
PaperPDFCodeCodeCodeCodeCodeCode(official)

Abstract

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.

Results

TaskDatasetMetricValueModel
Image ClassificationCIFAR-10Percentage correct60.6ANODE
Image ClassificationMNISTAccuracy99.63Augmented Neural Ordinary Differential Equation
Image ClassificationMNISTPercentage error0.37Augmented Neural Ordinary Differential Equation
Image ClassificationMNISTAccuracy98.2ANODE
Image ClassificationMNISTPercentage error1.8ANODE
Image ClassificationSVHNPercentage error16.5ANODE

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