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We introduce a simple and effective method for regularizing large convolutional neural networks.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Best practices for convolutional neural networks applied to visual document analysis
P. Simard, D. Steinkraus, and J. Platt · 2003
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What is the best multi-stage architecture for object recognition?
K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun · 2009
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Learning multiple layers of featurs from tiny images
A. Krizhevsky · 2009
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Rectified linear units improve restricted boltzmann machines
V. Nair and G.E. Hinton · 2010
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Flexible, high performance convolutional neural networks for image classification
D. C. Cireşan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber · 2011
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Beyond spatial pyramids: Receptive field learning for pooled image features
Y. Jia and C. Huang · 2011
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Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
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Adaptive deconvolutional networks for mid and high level feature learning
M. Zeiler, G. Taylor, and R. Fergus · 2011
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Improving neural networks by preventing co-adaptation of feature detectors
G.E. Hinton, N. Srivastave, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov · 2012
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cuda-convnet
A. Krizhevsky · 2012
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The MNIST database
Y. LeCun · 2012
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Neural Networks: Tricks of the Trade
G. Montavon, G. Orr, and K.-R. Muller, editors · 2012
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Convolutional neural networks applied to house numbers digit classification
P. Sermanet, S. Chintala, and Y. LeCun · 2012
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