Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Abstract

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural… (More)
DOI: 10.1007/978-3-319-63387-9_5

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Cite this paper

@inproceedings{Katz2017ReluplexAE, title={Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks}, author={Guy Katz and Clark W. Barrett and David L. Dill and Kyle Julian and Mykel J. Kochenderfer}, booktitle={CAV}, year={2017} }