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Batch Normalization is quite effective at accelerating and improving the training of deep models.
Neighbourhood components analysis
J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov · 2004
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Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
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Lecture 6.5 - rmsprop
T. Tieleman and G. Hinton · 2012
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ImageNet Large Scale Visual Recognition Challenge, 2014
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 · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
Cited alongside, same era.
D. Arpit, Y. Zhou, B. U. Kota, and V. Govindaraju · 2016
Cited alongside, same era.
J. L. Ba, J. R. Kiros, and G. E. Hinton · 2016
Cited alongside, same era.
Revisiting distributed synchronous sgd
J. Chen, R. Monga, S. Bengio, and R. Jozefowicz · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Later among the works it cites.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
T. Salimans and D. P. Kingma · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Later among the works it cites.
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