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We investigate a new method to augment recurrent neural networks with extra memory without increasing the number of network parameters.
An efficient gradient-based algorithm for on-line training of recurrent network trajectories
Williams, Ronald J and Peng, Jing · 1990
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Long short-term memory
Hochreiter, Sepp and Schmidhuber, Jürgen · 1997
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Learning to forget: Continual prediction with lstm
Gers, Felix A, Schmidhuber, Jürgen, and Cummins, Fred · 2000
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Holographic reduced representation: Distributed representation for cognitive structures
Plate, Tony · 2003
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Generating text with recurrent neural networks
Sutskever, Ilya, Martens, James, and Hinton, Geoffrey E · 2011
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Graves, Alex, Wayne, Greg, and Danihelka, Ivo · 2014
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Sutskever, Ilya, Vinyals, Oriol, and Le, Quoc VV · 2014
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Grefenstette, Edward, Hermann, Karl Moritz, Suleyman, Mustafa, and Blunsom, Phil · 2015
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Inferring algorithmic patterns with stack-augmented recurrent nets
Joulin, Armand and Mikolov, Tomas · 2015
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An empirical exploration of recurrent network architectures
Jozefowicz, Rafal, Zaremba, Wojciech, and Sutskever, Ilya · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng, Karpathy, Andrej, Khosla, Aditya, Bernstein, Michael, Berg, Alexander C., and Fei-Fei, Li · 2015
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End-to-end memory networks
Sukhbaatar, Sainbayar, Weston, Jason, Fergus, Rob, et al · 2015
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Arjovsky, Martin, Shah, Amar, and Bengio, Yoshua · 2015
Cited alongside, same era.
Zaremba, Wojciech and Sutskever, Ilya · 2015
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