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# Simple Recurrent Networks Learn Context-Free and Context-Sensitive Languages by Counting

@article{Rodriguez2001SimpleRN, title={Simple Recurrent Networks Learn Context-Free and Context-Sensitive Languages by Counting}, author={Paul E. D. Soto Rodriguez}, journal={Neural Computation}, year={2001}, volume={13}, pages={2093-2118} }

- Published 2001 in Neural Computation
DOI:10.1162/089976601750399326

It has been shown that if a recurrent neural network (RNN) learns to process a regular language, one can extract a finite-state machine (FSM) by treating regions of phase-space as FSM states. However, it has also been shown that one can construct an RNN to implement Turing machines by using RNN dynamics as counters. But how does a network learn languages that require counting? Rodriguez, Wiles, and Elman (1999) showed that a simple recurrent network (SRN) can learn to process a simple context… CONTINUE READING

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