First-Order Recurrent Neural Networks and Deterministic Finite State Automata

By P. Manolios, Robert Fanelli. 1994

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Citations in the corpus

Deep learning in neural networks: An overview (2014, methodology): “LSTM clearly outperformed previous RNNs on tasks that require learning the rules of regular languages describable by deterministic Finite State Automata (FSAs) (Watrous and Kuhn, 1992; Casey, 1996; Siegelmann, 1992; Blair and Pollack, 1997; Kalinke and Lehmann, 1998; Zeng et al., 1994; Manolios and ”
Architectural Bias in Recurrent Neural Networks: Fractal Analysis (2002, background): “However, it has been known for some time that when training RNNs to process symbolic sequences, activations of recurrent units display a considerable amount of structural diﬁerentiation even prior to learning (Christiansen & Chater, 1999; Kolen, 1994; Kolen, 1994a; Manolios & Fanelli, 1994).”
Learning to count without a counter: A case study of dynamics and activation landscapes in recurrent networks (1995, unknown): “…(e.g., of the sort reported in Cleeremans, Servan-Schreiber, & McClelland, 1989; Elman, 1991; Giles, Miller, Chen, Chen, Sun, & Lee, 1992; Manolios & Fanelli, 1994; Watrous & Kuhn, 1992) or else to establish theoretical capacity through formal analysis (Kolen, 1994; Pollack, 1991;…”
Bridging Long Time Lags by Weight Guessing and \Long Short Term Memory" (1996, unknown): “See, e.g., [2, 32, 22, 17, 16].”
Extraction of rules from discrete-time recurrent neural networks (1996, background): “The performance of recurrent networks on long temporal sequences is naturally explored by using grammatical strings (Manolios & Fanelli, 1994).”
Rule Extraction from Recurrent Neural Networks: ATaxonomy and Review (2005, methodology): “…2003), SOM (Tiˇno & ˇSajda 1995, Tiˇno & Vojtek 1998, Blanco et al. 2000), dynamical cell structures (Tiˇno & K¨oteles 1999), “other” vector quantiﬁers (Manolios & Fanelli 1994), hierarchical clustering (Alqu´ezar & Sanfeliu 1994 a ), dynamically updated intervals (Watrous & Kuhn 1992) and…”
Analysis of Dynamical Recognizers (1997, methodology): “One of the principal themes has been the use of clustering and minimization techniques to extract a Finite State Automaton (FSA) that approximates the network's behavior (Giles et al., 1992, Manolios & Fanelli, 1994, Ti no & Sajda, 1995).”
Asynchronous translations with recurrent neural nets (1997, background): “In recent years, there has been a lot of interest in training discrete-time recurrent neural networks (DTRNN) to behave as finite-state machines [7, 9, 13, 17, 18, 20].”