# Incorrect by Construction: Fine Tuning Neural Networks for Guaranteed Performance on Finite Sets of Examples

@article{Papusha2020IncorrectBC, title={Incorrect by Construction: Fine Tuning Neural Networks for Guaranteed Performance on Finite Sets of Examples}, author={Ivan Papusha and Rosa Wu and Joshua Brul'e and Yanni Kouskoulas and Daniel Genin and Aurora C. Schmidt}, journal={ArXiv}, year={2020}, volume={abs/2008.01204} }

There is great interest in using formal methods to guarantee the reliability of deep neural networks. However, these techniques may also be used to implant carefully selected input-output pairs. We present initial results on a novel technique for using SMT solvers to fine tune the weights of a ReLU neural network to guarantee outcomes on a finite set of particular examples. This procedure can be used to ensure performance on key examples, but it could also be used to insert difficult-to-find…

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