Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples

@InProceedings{pmlr-v80-weiss18a,
  title = 	 {Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples},
  author =       {Weiss, Gail and Goldberg, Yoav and Yahav, Eran},
  booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
  pages = 	 {5247--5256},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
  volume = 	 {80},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--15 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v80/weiss18a/weiss18a.pdf},
  url = 	 {https://proceedings.mlr.press/v80/weiss18a.html},
  abstract = 	 {We present a novel algorithm that uses exact learning and abstraction to extract a deterministic finite automaton describing the state dynamics of a given trained RNN. We do this using Angluin’s \lstar algorithm as a learner and the trained RNN as an oracle. Our technique efficiently extracts accurate automata from trained RNNs, even when the state vectors are large and require fine differentiation.}
}