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A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams and David Zipser · 1989
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Tangent prop - a formalism for specifying selected invariances in an adaptive network
Patrice Y. Simard, Bernard Victorri, Yann LeCun, and John S. Denker · 1991
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Gradient calculations for dynamic recurrent neural networks: A survey
Barak A Pearlmutter · 1995
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Long short-term memory learns context free and context sensitive languages
Felix A Gers and Jürgen Schmidhuber · 2001
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Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach, 2002
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A Monte Carlo EM approach for partially observable diffusion processes: Theory and applications to neural networks
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Backpropagation-decorrelation: online recurrent learning with O(N) complexity
Jochen J. Steil · 2004
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John Duchi, Elad Hazan, and Yoram Singer · 2010
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Training recurrent networks online without backtracking
Yann Ollivier, Corentin Tallec, and Guillaume Charpiat · 2015
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Memory-efficient backpropagation through time
Audrunas Gruslys, Rémi Munos, Ivo Danihelka, Marc Lanctot, and Alex Graves · 2016
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Decoupled neural interfaces using synthetic gradients
Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, and Koray Kavukcuoglu · 2016
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