City Research Online

Abstract

Recurrent Neural Networks (RNNs) have achieved remarkable success in handling sequential data. However, they lack interpretability. Extracting Deterministic Finite Automata (DFAs) from black-box models can provide insight into their decision-making processes. This research focuses on extracting DFAs from RNNs trained on regular languages using an exact learning framework. The proposed approach employs the L algorithm to learn a DFA, and it demonstrates how a hyperplane-based method can be used to partition the RNN state space when evaluating equivalence queries.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record will be available online at: https://link.springer.com/series/7899
Publisher Keywords:	Recurrent Neural Networks, Finite State Automata, Explainable AI
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

Export

Downloads