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Deep neural networks are state of the art methods for many learning tasks due to their ability to extract increasingly better features at each network layer.
Gradient-based learning applied to document recognition
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Model compression
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Meta-recognition: The theory and practice of recognition score analysis
W. J. Scheirer, A. Rocha, R. J. Micheals, and T. E. Boult · 2011
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Improving the speed of neural networks on cpus
V. Vanhoucke, A. Senior, and M. Z. Mao · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Efficient backprop
Y. A. LeCun, L. Bottou, G. B. Orr, and K.-R. Müller · 2012
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Fast training of convolutional networks through ffts
M. Mathieu, M. Henaff, and Y. LeCun · 2013
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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Very deep convolutional networks for large-scale image recognition
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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Predicting failures of vision systems
P. Zhang, J. Wang, A. Farhadi, M. Hebert, and D. Parikh · 2014
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Towards open set deep networks
A. Bendale and T. Boult · 2015
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Towards open world recognition
A. Bendale and T. Boult · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Compression of deep convolutional neural networks for fast and low power mobile applications
Y.-D. Kim, E. Park, S. Yoo, T. Choi, L. Yang, and D. Shin · 2015
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Fast algorithms for convolutional neural networks
A. Lavin · 2015
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R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
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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 · 2015
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S. Han, H. Mao, and W. J. Dally · 2015
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Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Deep networks with stochastic depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Weinberger · 2016
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Conditional deep learning for energy-efficient and enhanced pattern recognition
P. Panda, A. Sengupta, and K. Roy · 2016
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Branchynet: Fast inference via early exiting from deep neural networks
S. Teerapittayanon, B. McDanel, and H. Kung · 2016
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