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Recurrent Neural Networks (RNNs) are theoretically Turing-complete and established themselves as a dominant model for language processing.
Inductive inference of formal languages from positive data
Dana Angluin. 1980 · 1980
Earlier work this paper cites.
Distributed representations, simple recurrent networks, and grammatical structure
Jeffrey L Elman. 1991 · 1991
Earlier work this paper cites.
Learning context-free grammars: Capabilities and limitations of a recurrent neural network with an external stack memory
Sreerupa Das, C Lee Giles, and Guo-Zheng Sun. 1992 · 1992
Earlier work this paper cites.
Learning and extracting finite state automata with second-order recurrent neural networks
C Lee Giles, Clifford B Miller, Dong Chen, Hsing-Hen Chen, Guo-Zheng Sun, and Yee-Chun Lee. 1992 · 1992
Earlier work this paper cites.
On the computational power of neural nets
Hava T Siegelmann and Eduardo D Sontag. 1992 · 1992
Earlier work this paper cites.
Tail-recursive distributed representations and simple recurrent networks
Stan C Kwasny and Barry L Kalman. 1995 · 1995
Earlier work this paper cites.
Computation beyond the Turing limit
Hava T Siegelmann. 1995 · 1995
Earlier work this paper cites.
Learning to count without a counter: A case study of dynamics and activation landscapes in recurrent networks
Janet Wiles and Jeff Elman. 1995 · 1995
Earlier work this paper cites.
The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction
Mike Casey. 1996 · 1996
Earlier work this paper cites.
A recurrent network that performs a context-sensitive prediction task
Mark Steijvers and Peter Grünwald. 1996 · 1996
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Cited alongside, same era.
Designing a counter: Another case study of dynamics and activation landscapes in recurrent networks
Steffen Hölldobler, Yvonne Kalinke, and Helko Lehmann. 1997 · 1997
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A Recurrent Neural Network that learns to count
Paul Rodriguez, Janet Wiles, and Jeffrey L. Elman. 1999 · 1999
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Context-free and context-sensitive dynamics in recurrent neural networks
Mikael Bodén and Janet Wiles. 2000 · 2000
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LSTM recurrent networks learn simple context-free and context-sensitive languages
Felix A Gers and E Schmidhuber. 2001 · 2001
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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Long short-term memory recurrent neural network architectures for large scale acoustic modeling
Hasim Sak, Andrew W. Senior, and Françoise Beaufays. 2014 · 2014
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Sequence to Sequence Learning with Neural Networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
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LSTM: A search space odyssey
Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber. 2017 · 2017
Later among the works it cites.
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
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Mikael Bodén and Janet Wiles. 2002 · 2002
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Incremental training of first order recurrent neural networks to predict a context-sensitive language
Stephan K Chalup and Alan D Blair. 2003 · 2003
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Nelson F. Liu, Omer Levy, Roy Schwartz, Chenhao Tan, and Noah A. Smith. 2018 · 2018
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Gail Weiss, Yoav Goldberg, and Eran Yahav. 2018 · 2018
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Simple recurrent networks learn context-free and context-sensitive languages by counting
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