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While Recurrent Neural Networks (RNNs) are famously known to be Turing complete, this relies on infinite precision in the states and unbounded computation time.
Counter machines and counter languages
Patrick C. Fischer, Albert R. Meyer, and Arnold L. Rosenberg. 1968 · 1968
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Finding Structure in Time
Jeffrey L. Elman. 1990 · 1990
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On the computational power of neural nets
Hava T. Siegelmann and Eduardo D. Sontag. 1992 · 1992
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Analog computation via neural networks
Hava T. Siegelmann and Eduardo D. Sontag. 1994 · 1994
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Recurrent neural networks and finite automata
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Neural Networks and Analog Computation: Beyond the Turing Limit , 1 edition
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Recurrent nets that time and count
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Lstm recurrent networks learn simple context-free and context-sensitive languages
F. A. Gers and E. Schmidhuber. 2001 · 2001
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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
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Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, and Geoffrey Hinton. 2015 · 2015
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Parsing as language modeling
Do Kook Choe and Eugene Charniak. 2016 · 2016
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Binarized neural networks
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2016 · 2016
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Towards string-to-tree neural machine translation
Roee Aharoni and Yoav Goldberg. 2017 · 2017
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Recurrent neural networks as weighted language recognizers
Yining Chen, Sorcha Gilroy, Kevin Knight, and Jonathan May. 2017 · 2017
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Lstm: A search space odyssey
K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber. 2017 · 2017
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Quoc V. Le, Navdeep Jaitly, and Geoffrey E. Hinton. 2015 · 2015
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