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We introduce an exceptionally simple gated recurrent neural network (RNN) that achieves performance comparable to well-known gated architectures, such as LSTMs and GRUs, on the word-level language modeling task.
A two-dimensional mapping with a strange attractor
Michel Hénon · 1976
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Building a large annotated corpus of english: The penn treebank
Mitchell P Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini · 1993
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Learning to forget: Continual prediction with lstm
Felix A Gers, Jürgen Schmidhuber, and Fred Cummins · 2000
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Real-time computation at the edge of chaos in recurrent neural networks
Nils Bertschinger and Thomas Natschläger · 2004
Earlier work this paper cites.
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
Cited alongside, same era.
Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks
David Sussillo and Omri Barak · 2013
Cited alongside, same era.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Cited alongside, same era.
Learning longer memory in recurrent neural networks
Tomas Mikolov, Armand Joulin, Sumit Chopra, Michael Mathieu, and Marc’Aurelio Ranzato · 2014
Cited alongside, same era.
Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering
Steven H Strogatz · 2014
Later among the works it cites.
Recurrent neural network regularization
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals · 2014
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An empirical exploration of recurrent network architectures
Rafal Jozefowicz, Wojciech Zaremba, and Ilya Sutskever · 2015
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End-to-end memory networks
Sainbayar Sukhbaatar, Jason Weston, Rob Fergus, et al · 2015
Later among the works it cites.
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