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Building large models with parameter sharing accounts for most of the success of deep convolutional neural networks (CNNs).
Learning invariant features through topographic filter maps
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Adadelta: an adaptive learning rate method
Matthew D Zeiler · 2012
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Ian J Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and Yoshua Bengio · 2013
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Deep symmetry networks
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Yu Cheng, Felix X. Yu, Rogerio Feris, Sanjiv Kumar, and Shih-Fu Chang · 2015
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Deep fried convnets
Zichao Yang, Marcin Moczulski, Misha Denil, Nando de Freitas, Alex Smola, Le Song, and Ziyu Wang · 2015
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Tensorizing neural networks
Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, and Dmitry Vetrov · 2015
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Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2015
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Exploiting cyclic symmetry in convolutional neural networks
Sander Dieleman, Jeffrey De Fauw, and Koray Kavukcuoglu · 2016
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Sergey Ioffe and Christian Szegedy · 2015
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