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As a widely used non-linear activation, Rectified Linear Unit (ReLU) separates noise and signal in a feature map by learning a threshold or bias.
Reducing the dimensionality of data with neural networks
Hinton, G. E. and Salakhutdinov, R. R · 2006
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Learning multiple layers of features from tiny images
Krizhevsky, Alex and Hinton, Geoffrey · 2009
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Rectifier nonlinearities improve neural network acoustic models
Mass, Andrew L., Hannum, Awin Y., and Ng, Andew Y · 2010
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Rectified linear units improve restricted boltzmann machines
Nair, Vinod and Hinton, Geoffrey E · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Yuval, Wang, Tao, Coates, Adam, Bissacco, Alessandro, Wu, Bo, and Ng, Andrew Y · 2011
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Deep neural networks for acoustic modeling in speech recognition
Hinton, Geoffrey E., Deng, Li, Yu, Dong, Dahl, George E., Mohamed, Abdel-rahman, Jaitly, Navdeep, Senior, Andrew, Vanhoucke, Vincent, Nguyen, Patrick, Sainath, Tara N., and Kingsbury, Brian · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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Invariant scattering convolution networks
Bruna, Joan and Mallat, Stephane · 2013
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Maxout networks
Goodfellow, Ian J., Warde-farley, David, Mirza, Mehdi, Courville, Aaron, and Bengio, Yoshua · 2013
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Regularization of neural networks using dropconnect
Wan, Li, Zeiler, Matthew, Zhang, Sixin, Cun, Yann Le, and Fergus, Rob · 2013
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From maxout to channel-out: Encoding information on sparse pathways
Wang, Qi and JaJa, Joseph · 2013
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Stochastic pooling for regularization of deep convolutional neural networks
Zeiler, Matthew D. and Fergus, Rob · 2013
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Deeply-supervised nets
Lee, Chen-Yu, Xie, Saining, Gallagher, Patrick, Zhang, Zhengyou, and Tu, Zhuowen · 2014
Cited alongside, same era.
Network in network
Lin, Min, Chen, Qiang, and Yan, Shuicheng · 2014
Cited alongside, same era.
Learning Activation Functions to Improve Deep Neural Networks
Agostinelli, Forest, Hoffman, Matthew, Sadowski, Peter, and Baldi, Pierre · 2015
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Fast and accurate deep network learning by exponential linear units
Clevert, Djork-Arne, Unterthiner, Thomas, and Hochreiter, Sepp · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. E · 2015
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Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree
Lee, Chen-Yu, Gallagher, Patrick W., and Tu, Zhuowen · 2015
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Scalable bayesian optimization using deep neural networks
Snoek, Jasper, Rippel, Oren, Swersky, Kevin, Kiros, Ryan, Satish, Nadathur, Sundaram, Narayanan, Patwary, Mostofa, Prabhat, Mr, and Adams, Ryan · 2015
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan · 2014
Cited alongside, same era.
Deep networks with internal selective attention through feedback connections
Stollenga, Marijn F, Masci, Jonathan, Gomez, Faustino, and Schmidhuber, Juergen · 2014
Cited alongside, same era.
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Empirical evaluation of rectified activations in convolution network
Xu, Bing, Wang, Naiyan, Chen, Tianqi, and Li, Mu · 2015
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Mastering the game of go with deep neural networks and tree search
Silver, David, Huang, Aja, Maddison, Chris J., Guez, Arthur, Sifre, Laurent, van den Driessche, George, Schrittwieser, Julian, Antonoglou, Ioannis, Panneershelvam, Veda, Lanctot, Marc, Dieleman, Sander, Grewe, Dominik, Nham, John, Kalchbrenner, Nal, Sutskever, Ilya, Lillicrap, Timothy, Leach, Madeleine, Kavukcuoglu, Koray, Graepel, Thore, and Hassabis, Demis · 2016
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