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Dropout-based regularization methods can be regarded as injecting random noise with pre-defined magnitude to different parts of the neural network during training.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Regularization of neural networks using dropconnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann L Cun, and Rob Fergus · 2013
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Fast dropout training
Sida I Wang and Christopher D Manning · 2013
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Speeding up convolutional neural networks with low rank expansions
Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Song Han, Huizi Mao, and William J Dally · 2015
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Variational dropout and the local reparameterization trick
Diederik P Kingma, Tim Salimans, and Max Welling · 2015
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Smart regularization of deep architectures
Christos Louizos · 2015
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Tensorizing neural networks
Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, and Dmitry P Vetrov · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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92.45 on cifar-10 in torch, 2015
Sergey Zagoruyko · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
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Fast convnets using group-wise brain damage
Vadim Lebedev and Victor Lempitsky · 2016
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Dropout-based automatic relevance determination
Dmitry Molchanov, Arseniy Ashuha, and Dmitry Vetrov · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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Michael Figurnov, Maxwell D Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, and Ruslan Salakhutdinov · 2016
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Perforatedcnns: Acceleration through elimination of redundant convolutions
Mikhail Figurnov, Aizhan Ibraimova, Dmitry P Vetrov, and Pushmeet Kohli · 2016
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Ultimate tensorization: compressing convolutional and fc layers alike
Timur Garipov, Dmitry Podoprikhin, Alexander Novikov, and Dmitry Vetrov · 2016
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Theano: a cpu and gpu math expression compiler
Bergstra James, Breuleux Olivier, Bastien Frédéric, Lamblin Pascal, and Pascanu Razvan
Cited in the paper.
Ekaterina Lobacheva, Nadezhda Chirkova, and Dmitry Vetrov · 2017
Closest in time.
Variational dropout sparsifies deep neural networks
Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2017
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Soft weight-sharing for neural network compression
Karen Ullrich, Edward Meeds, and Max Welling · 2017
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