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Regularization is key for deep learning since it allows training more complex models while keeping lower levels of overfitting.
Simplifying neural networks by soft weight-sharing
Steven J. Nowlan and Geoffrey E. Hinton · 1992
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Slow, decorrelated features for pretraining complex cell-like networks
Yoshua Bengio and James S Bergstra · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Incoherent training of deep neural networks to de-correlate bottleneck features for speech recognition
Yebo Bao, Hui Jiang, Lirong Dai, and Cong Liu · 2013
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Maxout networks
Ian Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and Yoshua Bengio · 2013
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M. Saxe, James L. McClelland, and Surya Ganguli · 2013
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Regularization of neural networks using dropconnect
Li Wan, Matthew D Zeiler, Sixin Zhang, Yann L Cun, and Rob Fergus · 2013
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Benjamin Graham · 2014
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Min Lin, Qiang Chen, and Shuicheng Yan · 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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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 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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Negative correlations in visual cortical networks
Mircea I Chelaru and Valentin Dragoi · 2016
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Fast and accurate deep network learning by exponential linear units (ELUs)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2016
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Reducing overfitting in deep networks by decorrelating representations
Michael Cogswell, Faruk Ahmed, Ross Girshick, Larry Zitnick, and Dhruv Batra · 2016
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Weinberger · 2016
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Deeply-supervised nets
Chen-Yu Lee, Saining Xie, Patrick W. Gallagher, Zhengyou Zhang, and Zhuowen Tu · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Striving for simplicity: The all convolutional net
Jost T. Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2015
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Training very deep networks
Rupesh K. Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
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Hongyang Li, Wanli Ouyang, and Xiaogang Wang · 2016
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All you need is a good init
Dmytro Mishkin and Jiri Matas · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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