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Training directed neural networks typically requires forward-propagating data through a computation graph, followed by backpropagating error signal, to produce weight updates.
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., and Williams, R. J · 1986
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Learning to predict by the methods of temporal differences
Sutton, R S · 1988
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A learning algorithm for continually running fully recurrent neural networks
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Networks adjusting networks
Schmidhuber, Jürgen · 1990
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Approximating dynamic programming for real-time control and neural modeling
Werbos, P J · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
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Building a large annotated corpus of english: The penn treebank
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Reinforcement learning: An introduction, 1998
Sutton, R S and Barto, A G · 1998
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Direct gradient-based reinforcement learning
Baxter, J. and Bartlett, P. L · 2000
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Learning multiple layers of features from tiny images, 2009
Krizhevsky, A. and Hinton, G · 2009
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Deep sparse rectifier neural networks
Glorot, X., Bordes, A., and Bengio, Y · 2011
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Policy gradient coagent networks
Thomas, P. S · 2011
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Generating sequences with recurrent neural networks
Graves, A · 2013
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How auto-encoders could provide credit assignment in deep networks via target propagation
Bengio, Y · 2014
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Distributed optimization of deeply nested systems
Carreira-Perpinán, M A and Wang, W · 2014
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Difference target propagation
Lee, D., Zhang, S., Fischer, A., and Bengio, Y · 2015
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Training recurrent networks online without backtracking
Ollivier, Y. and Charpiat, G · 2015
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Cooijmans, T., Ballas, N., Laurent, C., and Courville, A · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T P, Cownden, D, Tweed, D B, and Akerman, C J · 2016
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Value-gradient learning
Fairbank, M · 2014
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Graves, A., Wayne, G., and Danihelka, I · 2014
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2014
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Learning continuous control policies by stochastic value gradients
Heess, N, Wayne, G, Silver, D, Lillicrap, T P, Erez, T, and Tassa, Y · 2015
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Kickback cuts backprop’s red-tape: Biologically plausible credit assignment in neural networks
Balduzzi, D., Vanchinathan, H., and Buhmann, J
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Kickback cuts backprop’s red-tape: Biologically plausible credit assignment in neural networks
Balduzzi, D, Vanchinathan, H, and Buhmann, J
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Nøkland, A · 2016
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Training neural networks without gradients: A scalable admm approach
Taylor, G, Burmeister, R, Xu, Z, Singh, B, Patel, A, and Goldstein, T · 2016
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Understanding synthetic gradients and decoupled neural interfaces
Czarnecki, W M, Swirszcz, G, Jaderberg, M, Osindero, S, Vinyals, O, and Kavukcuoglu, K · 2017
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Unbiased online recurrent optimization
Tallec, C. and Ollivier, Y · 2017
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