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The neural network pushdown automaton: Architecture, dynamics and training

https://doi.org/10.1007/bfb0054003
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Second-order recurrent neural networks can learn regular grammars from noisy strings
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Distributed representations, simple recurrent networks, and grammatical structure
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Representation of Finite State Automata in Recurrent Radial Basis Function Networks
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Introduction to formal language theory
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Machine Learning Using a Higher Order Correlation Network
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Finding structure in time
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Higher order associative memories and their optical implementations
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Using recurrent neural networks to learn the structure of interconnection networks
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Learning a class of large finite state machines with a recurrent neural network
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Extraction of rules from discrete-time recurrent neural networks
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Complexity of automaton identification from given data
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Dynamical recognizers: real-time language recognition by analog computers
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On the Computational Power of Recurrent Neural Networks for Structures
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Switching and Finite Automata Theory
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Neural networks and physical systems with emergent collective computational abilities.
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Syntactic Neural Networks
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Connectionist Language Users
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Extraction, Insertion and Refinement of Symbolic Rules in Dynamically Driven Recurrent Neural Networks
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Computational capabilities of recurrent NARX neural networks
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Unified integration of explicit knowledge and learning by example in recurrent networks
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Translation, rotation, and scale invariant pattern recognition by high-order neural networks and moment classifiers
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Locally recurrent globally feedforward networks: a critical review of architectures
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Discrete recurrent neural networks for grammatical inference
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First-order versus second-order single-layer recurrent neural networks
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Constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution
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Stability properties of labeling recursive auto-associative memory
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Computational capabilities of local-feedback recurrent networks acting as finite-state machines
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Time-delay neural networks: representation and induction of finite-state machines
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The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks
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A neural-network architecture for syntax analysis
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Identification and control of dynamical systems using neural networks
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Fuzzy finite-state automata can be deterministically encoded into recurrent neural networks
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Turing equivalence of neural networks with second order connection weights
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A learning method for recurrent networks based on minimization of finite automata
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Syntactic pattern recognition and applications
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Pattern classification and scene analysis
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Understanding Neural Networks for Grammatical Inference and Recognition
inferredcites →https://doi.org/10.1142/s0129065792000255openalex-snapshot
EFFICIENT HIGHER-ORDER NEURAL NETWORKS FOR CLASSIFICATION AND FUNCTION APPROXIMATION
inferredcites →https://doi.org/10.1142/s0218001493000431openalex-snapshot
EXPERIMENTAL COMPARISON OF THE EFFECT OF ORDER IN RECURRENT NEURAL NETWORKS
inferredcites →https://doi.org/10.1145/130385.130390openalex-snapshot
Random DFA's can be approximately learned from sparse uniform examples
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Constructing deterministic finite-state automata in recurrent neural networks
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Finite State Automata and Simple Recurrent Networks
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Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks
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Induction of Finite-State Languages Using Second-Order Recurrent Networks
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Connectionist models for natural language processing
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Adaptive pattern recognition and neural networks
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Parallel Distributed Processing

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Synthesizing Context-free Grammars from Recurrent Neural Networks
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The Underlying Formal Model of Algorithmic Lateral Inhibition in Motion Detection
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Adaptive Processing of Sequences and Data Structures
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Recursive principal components analysis
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Formal languages aspects as a tool for representation and implementation of behavior-based robotics
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Rule Extraction from Recurrent Neural Networks: ATaxonomy and Review
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Dynamic Neural Turing Machine with Continuous and Discrete Addressing Schemes
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Real-Time Accumulative Computation Motion Detectors
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Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes
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Recognizing Long Grammatical Sequences Using Recurrent Networks Augmented With An External Differentiable Stack
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Synthesizing Context-free Grammars from Recurrent Neural Networks (Extended Version)
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Neural networks as cellular computing models for temporal sequence processing.
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Verification of Infinite-State Systems and Machine Learning
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Biological Foundations and Origin of Syntax
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