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The backpropagation algorithm has long been the canonical training method for neural networks.
Learning representations by back propagating errors
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams · 1986
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Learning process in an asymmetric threshold network
Yann Le Cun · 1986
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Competitive learning: From interactive activation to adaptive resonance
Stephen Grossberg · 1987
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The recent excitement about neural networks
F. H. C. Crick · 1989
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Contrastive hebbian learning in the continuous hopfield model
Javier R. Movellan · 1991
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Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm
R. C. O’Reilly · 1996
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Deep boltzmann machines
Ruslan R. Salakhutdinov and Geoffrey E. Hinton · 2009
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On random weights and unsupervised feature learning
Andrew M. Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, and Andrew Y. Ng · 2011
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Rectifier nonlinearities improve neural network acoustic models
Andrew L. Maas · 2013
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Fastfood - computing hilbert space expansions in loglinear time
Quoc V. Le, Tamás Sarlós, and Alexander J. Smola · 2013
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How auto-encoders could provide credit assignment in deep networks via target propagation
Yoshua Bengio · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan R. Salakhutdinov · 2014
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Difference target propagation
Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, and Yoshua Bengio · 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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep fried convnets
Acdc: A structured efficient linear layer
Marcin Moczulski, Misha Denil, Jeremy Appleyard, and Nando de Freitas · 2016
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Sobolev training for neural networks
Wojciech M Czarnecki, Simon Osindero, Max Jaderberg, Grzegorz Swirszcz, and Razvan Pascanu · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Assessing the scalability of biologically-motivated deep learning algorithms and architectures
Sergey Bartunov, Adam Santoro, Blake Richards, Luke Marris, Geoffrey E Hinton, and Timothy Lillicrap · 2018
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How does batch normalization help optimization?
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry · 2018
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Zichao Yang, Marcin Moczulski, Misha Denil, Nando de Freitas, Alexander J. Smola, Le Song, and Ziyu Wang · 2015
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Decoupled neural interfaces using synthetic gradients
Max Jaderberg, Wojciech Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, David Silver, and Koray Kavukcuoglu · 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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How important is weight symmetry in backpropagation?
Qianli Liao, Joel Z Leibo, and Tomaso Poggio · 2016
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Direct feedback alignment provides learning in deep neural networks
Arild Nøkland · 2016
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Neural networks for machine learning, Coursera videolecture 15b
G. Hinton
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Arild Nøkland and Lars Hiller Eidnes · 2019
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Biologically-plausible learning algorithms can scale to large datasets
Will Xiao, Honglin Chen, Qianli Liao, and Tomaso Poggio · 2019
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Deep learning without weight transport
Mohamed Akrout, Collin Wilson, Peter C. Humphreys, Timothy P. Lillicrap, and Douglas B. Tweed · 2019
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Direct feedback alignment with sparse connections for local learning
Brian Crafton, Abhinav Parihar, Evan Gebhardt, and Arijit Raychowdhury · 2019
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Exponential convergence rates for batch normalization: The power of length-direction decoupling in non-convex optimization
Jonas Kohler, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann, Ming Zhou, and Klaus Neymeyr · 2019
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