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Modern computer vision is all about the possession of powerful image representations.
Some methods of speeding up the convergence of iteration methods
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A method of solving a convex programming problem with convergence rate o (1/k2)
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Gradient-based learning applied to document recognition
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Model compression
C. Buciluǎ, R. Caruana, and A. Niculescu-Mizil · 2006
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Caltech-256 object category dataset
G. Griffin, A. Holub, and P. Perona · 2007
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Learning multiple layers of features from tiny images, 2009
A. Krizhevsky and G. Hinton · 2009
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The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
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Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
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Theano: new features and speed improvements
F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. J. Goodfellow, A. Bergeron, N. Bouchard, and Y. Bengio · 2012
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Fast and adaptive online training of feature-rich translation models
S. Green, S. I. Wang, D. M. Cer, and C. D. Manning · 2013
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Do deep nets really need to be deep?
J. Ba and R. Caruana · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Dark knowledge
G. Hinton, O. Vinyals, and J. Dean · 2014
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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
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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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Bayesian dark knowledge
A. K. Balan, V. Rathod, K. P. Murphy, and M. Welling · 2015
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Transferring knowledge from a rnn to a dnn
W. Chan, N. R. Ke, and I. Lane · 2015
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Rmsprop and equilibrated adaptive learning rates for non-convex optimization
Y. N. Dauphin, H. de Vries, J. Chung, and Y. Bengio · 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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Fitnets: Hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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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, A. C. Berg, and L. Fei-Fei · 2015
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Recurrent neural network training with dark knowledge transfer
D. Wang, C. Liu, Z. Tang, Z. Zhang, and M. Zhao · 2015
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