2020
DOI: 10.48550/arxiv.2007.00147
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Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications

Abstract: Recent work has shown that it is possible to learn neural networks with provable guarantees on the output of the model when subject to input perturbations, however these works have focused primarily on defending against adversarial examples for image classifiers. In this paper, we study how these provable guarantees can be naturally applied to other real world settings, namely getting performance specifications for robust virtual sensors measuring fuel injection quantities within an engine. We first demonstrat… Show more

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References 49 publications
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