# Interpreting Finite Automata for Sequential Data

arXiv: https://arxiv.org/abs/1611.07100 · HTML: https://mlsanity.com/paper/1611.07100/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2016.

## Built on by (most cited first, 4 of 4)

- [Extracting Weighted Automata for Approximate Minimization in Language Modelling](https://mlsanity.com/paper/2106.02965/) (2021)
- [FlexFringe: Modeling Software Behavior by Learning Probabilistic Automata](https://mlsanity.com/paper/2203.16331/) (2022)
- [Learning state machines via efficient hashing of future traces](https://mlsanity.com/paper/2207.01516/) (2022)
- [PAC learning PDFA from data streams](https://mlsanity.com/paper/2604.02244/) (2026)

## Abstract (arXiv)

Automaton models are often seen as interpretable models. Interpretability itself is not well defined: it remains unclear what interpretability means without first explicitly specifying objectives or desired attributes. In this paper, we identify the key properties used to interpret automata and propose a modification of a state-merging approach to learn variants of finite state automata. We apply the approach to problems beyond typical grammar inference tasks. Additionally, we cover several use-cases for prediction, classification, and clustering on sequential data in both supervised and unsupervised scenarios to show how the identified key properties are applicable in a wide range of contexts.