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Rectified activation units (rectifiers) are essential for state-of-the-art neural networks.
Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
The Pascal Visual Object Classes (VOC) Challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
Earlier work this paper cites.
Deep sparse rectifier networks
X. Glorot, A. Bordes, and Y. Bengio · 2011
Earlier work this paper cites.
Multi-column deep neural networks for image classification
D. Ciresan, U. Meier, and J. Schmidhuber · 2012
Earlier work this paper cites.
Improving neural networks by preventing co-adaptation of feature detectors
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
Earlier work this paper cites.
Understanding deep architectures using a recursive convolutional network
D. Eigen, J. Rolfe, R. Fergus, and Y. LeCun · 2013
Earlier work this paper cites.
I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio · 2013
Earlier work this paper cites.
Some improvements on deep convolutional neural network based image classification
A. G. Howard · 2013
Earlier work this paper cites.
M. Lin, Q. Chen, and S. Yan · 2013
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Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
Cited alongside, same era.
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
A. M. Saxe, J. L. McClelland, and S. Ganguli · 2013
Cited alongside, same era.
Compete to compute
R. K. Srivastava, J. Masci, S. Kazerounian, F. Gomez, and J. Schmidhuber · 2013
Cited alongside, same era.
Regularization of neural networks using dropconnect
L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus · 2013
Cited alongside, same era.
On rectified linear units for speech processing
One weird trick for parallelizing convolutional neural networks
A. Krizhevsky · 2014
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C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu · 2014
Later among the works it cites.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2014
Later among the works it cites.
Overfeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2014
Later among the works it cites.
Very deep convolutional networks for large-scale image recognition
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M. D. Zeiler, M. Ranzato, R. Monga, M. Mao, K. Yang, Q. V. Le, P. Nguyen, A. Senior, V. Vanhoucke, J. Dean, and G. E. Hinton · 2013
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Learning activation functions to improve deep neural networks
F. Agostinelli, M. Hoffman, P. Sadowski, and P. Baldi · 2014
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Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Convolutional neural networks at constrained time cost
K. He and J. Sun · 2014
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Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Cited alongside, same era.
K. Simonyan and A. Zisserman · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Deep learning face representation by joint identification-verification
Y. Sun, Y. Chen, X. Wang, and X. Tang · 2014
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
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Deepface: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2014
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Visualizing and understanding convolutional neural networks
M. D. Zeiler and R. Fergus · 2014
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Deep image: Scaling up image recognition
R. Wu, S. Yan, Y. Shan, Q. Dang, and G. Sun · 2015
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