Work

Extracting Weighted Finite Automata from RNNs via iterative partitioning and spectral learning

Year: 2025

Type: conference-paper

Abstract: This paper proposes an algorithm to extract a Weighted Finite Automaton (WFA) from a Recurrent Neural Network (RNN), by adapting the algorithm in combination with spectral learning using Singular Value Decomposition (SVD). The method introduces a discrete abstraction of the RNN’s hidden state space to enable approximate equivalence queries within the framework. Through these queries, counterexamples are identified and used to iteratively refine the prefix and suffix sets that define the Hankel matrix, thereby improving the accuracy of successive WFA hypotheses. This work lays the groundwork for integrating discrete clustering with spectral learning in the context of WFA extraction from RNNs, an area that remains underexplored, with potential applications in sequence modelling and analysis. Future work will focus on implementing and empirically validating the proposed approach.

Source: City Research Online (City University London)

Authors: Wickramasinghe, S. Y., Howe, J. M., Daviaud, L.

Institutions: City St George's, University of London, University of East Anglia

Language: English

Topic: semigroups and automata theory

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Engineering and Physical Sciences Research Council

Award: Learning, Approximating and Minimising Streaming Automata for Large-scale Optimisation

OpenAlex ID: W7113040049