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Using back-propagation and its variants to train deep networks is often problematic for new users.
The organization of behavior; a neuropsycholocigal theory
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Stephen Grossberg · 1987
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Neurocomputing: Foundations of research
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams · 1988
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Generalization of backpropagation with application to a recurrent gas market model
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Contrastive hebbian learning in the continuous hopfield model
Javier R Movellan · 1991
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Simard, and Paolo Frasconi · 1994
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Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm
Randall C O’Reilly · 1996
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Sparse coding with an overcomplete basis set: A strategy employed by v1?
Bruno A Olshausen and David J Field · 1997
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Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
Rajesh PN Rao and Dana H Ballard · 1999
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Generalization in interactive networks: The benefits of inhibitory competition and hebbian learning
Randall C O’reilly · 2001
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Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
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Equivalence of backpropagation and contrastive hebbian learning in a layered network
Xiaohui Xie and H Sebastian Seung · 2003
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Greedy layer-wise training of deep networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, Hugo Larochelle, et al · 2007
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Understanding representations learned in deep architectures
Dumitru Erhan, Aaron Courville, and Yoshua Bengio · 2010
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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Predictive coding
Yanping Huang and Rajesh PN Rao · 2011
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Distributed optimization of deeply nested systems
Miguel Á. Carreira-Perpiñán and Weiran Wang · 2012
Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Random walks: Training very deep nonlinear feed-forward networks with smart initialization
David Sussillo · 2014
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Kickback cuts backprop’s red-tape: Biologically plausible credit assignment in neural networks
David Balduzzi, Hastagiri Vanchinathan, and Joachim M Buhmann · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Cited alongside, same era.
Predictive feedback and conscious visual experience
Matthew Panichello, Olivia Cheung, and Moshe Bar · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron C. Courville · 2013
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Whatever next? predictive brains, situated agents, and the future of cognitive science
Andy Clark · 2013
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The cerebellum as a neuronal machine
John C Eccles · 2013
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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
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Compete to compute
Rupesh K Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, and Jürgen Schmidhuber · 2013
Cited alongside, same era.
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Difference target propagation
Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, and Yoshua Bengio · 2015
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Dmytro Mishkin and Jiri Matas · 2015
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Online learning of deep hybrid architectures for semi-supervised categorization
Alexander G. Ororbia II, David Reitter, Jian Wu, and C. Lee Giles · 2015
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Hierarchical multiscale recurrent neural networks
Junyoung Chung, Sungjin Ahn, and Yoshua Bengio · 2016
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Noisy activation functions
Caglar Gulcehre, Marcin Moczulski, Misha Denil, and Yoshua Bengio · 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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How important is weight symmetry in backpropagation?
Qianli Liao, Joel Z Leibo, and Tomaso A Poggio · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
Timothy P Lillicrap, Daniel Cownden, Douglas B Tweed, and Colin J Akerman · 2016
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Direct feedback alignment provides learning in deep neural networks
Arild Nøkland · 2016
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Variational walkback: Learning a transition operator as a stochastic recurrent net
Anirudh Goyal Alias Parth Goyal, Nan Ke, Surya Ganguli, and Yoshua Bengio · 2017
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Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
Benjamin Scellier and Yoshua Bengio · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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