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Residual networks (ResNets) have recently achieved state-of-the-art on challenging computer vision tasks.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Learning to forget: Continual prediction with lstm
Felix A Gers, Jürgen Schmidhuber, and Fred Cummins · 2000
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky and Geoffrey Hinton · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Lecture 6.5-rmsprop
Tijmen Tieleman and Geoffrey Hinton · 2012
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M Saxe, James L McClelland, and Surya Ganguli · 2013
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
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Learning longer memory in recurrent neural networks
Tomas Mikolov, Armand Joulin, Sumit Chopra, Michael Mathieu, and Marc’Aurelio Ranzato · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin A. Riedmiller · 2014
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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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Nal Kalchbrenner, Ivo Danihelka, and Alex Graves · 2015
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Training very deep networks
Rupesh K Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
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