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Recurrent neural networks are powerful models for sequential data, able to represent complex dependencies in the sequence that simpler models such as hidden Markov models cannot handle.
Learning internal representations by error propagation
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1987
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Alpha-nets: A recurrent ’neural’ network architecture with a hidden Markov model interpretation
John S. Bridle · 1990
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Methods of information geometry
Shun-ichi Amari and Hiroshi Nagaoka · 1993
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Learning long-term dependencies with gradient descent is difficult
Y. Bengio, P. Simard, and P. Frasconi · 1994
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Hierarchical recurrent neural networks for long-term dependencies
Salah El Hihi and Yoshua Bengio · 1995
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Natural gradient works efficiently in learning
Shun-ichi Amari · 1998
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Adaptive method of realizing natural gradient learning for multilayer perceptrons
Shun-ichi Amari, Hyeyoung Park, and Kenji Fukumizu · 2000
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Tutorial on training recurrent neural networks, covering BPTT, RTRL, EKF and the “echo state network” approach
Herbert Jaeger · 2002
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Learning precise timing with lstm recurrent networks
Felix A. Gers, Nicol N. Schraudolph, and Jürgen Schmidhuber · 2003
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Topmoumoute online natural gradient algorithm
Nicolas Le Roux, Pierre-Antoine Manzagol, and Yoshua Bengio · 2007
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Deep learning via hessian-free optimization
James Martens · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
John C. Duchi, Elad Hazan, and Yoram Singer · 2011
Sequence transduction with recurrent neural networks
Alex Graves · 2012
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Training deep and recurrent neural networks with Hessian-free optimization
James Martens and Ilya Sutskever · 2012
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Generating sequences with recurrent neural networks, 2013
Alex Graves · 2013
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Riemannian metrics for neural networks I: feedforward networks
Yann Ollivier · 2013
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Revisiting natural gradient for deep networks
Razvan Pascanu and Yoshua Bengio · 2013
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Cited alongside, same era.
Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 – July 2, 2011
Lise Getoor and Tobias Scheffer, editors · 2011
Cited alongside, same era.
Learning recurrent neural networks with Hessian-free optimization
James Martens and Ilya Sutskever
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Generating text with recurrent neural networks
Ilya Sutskever, James Martens, and Geoffrey E. Hinton
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A clockwork RNN
Jan Koutník, Klaus Greff, Faustino J. Gomez, and Jürgen Schmidhuber · 2014
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