Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

@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}
}
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 networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit… CONTINUE READING
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