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We address the approximate minimization problem for weighted finite automata (WFAs) with weights in $\mathbb{R}$, over a one-letter alphabet: to compute the best possible approximation of a WFA given a bound on the number of states.
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Spectral Learning of Weighted Automata - A Forward–Backward Perspective
Borja Balle, Xavier Carreras, Franco M. Luque, and Ariadna Quattoni · 2014
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Methods of moments for learning stochastic languages: Unified presentation and empirical comparison
Borja Balle, William L. Hamilton, and Joelle Pineau · 2014
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Low-Rank Spectral Learning
Alex Kulesza, N. Raj Rao, and Satinder Singh · 2014
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A Canonical Form for Weighted Automata and Applications to Approximate Minimization
Borja Balle, Prakash Panangaden, and Doina Precup · 2015
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Low-Rank Spectral Learning with Weighted Loss Functions
Alex Kulesza, Nan Jiang, and Satinder Singh · 2015
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Bisimulation Metrics and Norms for Weighted Finite Automata
Borja Balle, Pascale Gourdeau, and Prakash Panangaden · 2017
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Approximation of Large-Scale Dynamical Systems
Athanasios C. Antoulas · 2005
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All Optimal Hankel–Norm Approximations and their Eerror Bounds in Discrete–Time
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Multitask Spectral Learning of Weighted Automata
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Explaining Black Boxes on Sequential Data Using Weighted Automata
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Singular Value Automata and Approximate Minimization
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Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning
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Learning Deterministic Weighted Automata with Queries and Counterexamples
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Approximate minimization of weighted tree automata
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Weighted Automata Extraction from Recurrent Neural Networks via Regression on State Spaces
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