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The deep learning community has devised a diverse set of methods to make gradient optimization, using large datasets, of large and highly complex models with deeply cascaded nonlinearities, practical.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
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Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
Kunihiko Fukushima · 1980
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Learning representations by back-propagating errors
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams · 1986
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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
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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A fast learning algorithm for deep belief nets
Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh · 2006
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Léon Bottou · 2010
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Understanding the difficulty of training deep feedforward neural networks
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Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
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Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan P. Adams · 2015
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Fast and accurate deep network learning by exponential linear units (ELUs)
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey E. Hinton · 2015
Cited alongside, same era.
Naman Agarwal, Brian Bullins, and Elad Hazan · 2016
Closest in time.