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Deep feedforward neural networks with piecewise linear activations are currently producing the state-of-the-art results in several public datasets.
“Learning representations by back-propagating errors,”
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams, · 1988
Earlier work this paper cites.
“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
Earlier work this paper cites.
“Rectified linear units improve restricted boltzmann machines,”
Vinod Nair and Geoffrey E Hinton, · 2010
Earlier work this paper cites.
“Reading digits in natural images with unsupervised feature learning,”
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng, · 2011
Earlier work this paper cites.
“Imagenet classification with deep convolutional neural networks,”
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, · 2012
Earlier work this paper cites.
“Rectifier nonlinearities improve neural network acoustic models,”
Andrew L Maas, Awni Y Hannun, and Andrew Y Ng, · 2013
Earlier work this paper cites.
“Compete to compute,”
Rupesh K Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, and Jürgen Schmidhuber, · 2013
Cited alongside, same era.
“Maxout networks,”
Ian J Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and Yoshua Bengio, · 2013
Cited alongside, same era.
“Network in network,”
Min Lin, Qiang Chen, and Shuicheng Yan, · 2013
Cited alongside, same era.
“Stochastic pooling for regularization of deep convolutional neural networks,”
Matthew D Zeiler and Rob Fergus, · 2013
Cited alongside, same era.
“Regularization of neural networks using dropconnect,”
Li Wan, Matthew Zeiler, Sixin Zhang, Yann L Cun, and Rob Fergus, · 2013
Cited alongside, same era.
“On the number of linear regions of deep neural networks,”
Guido F Montufar, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio, · 2014
Cited alongside, same era.
“Matconvnet – convolutional neural networks for matlab,”
A. Vedaldi and K. Lenc, · 2014
Later among the works it cites.
“Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2015
Closest in time.
“Understanding locally competitive networks,”
Rupesh Kumar Srivastava, Jonathan Masci, Faustino Gomez, and Jürgen Schmidhuber, · 2015
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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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“Deeply-supervised nets,”
Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, and Zhuowen Tu, · 2015
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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
Cited alongside, same era.
“Recurrent convolutional neural network for object recognition,”
Ming Liang and Xiaolin Hu, · 2015
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“Discriminative transfer learning with tree-based priors,”
Nitish Srivastava and Ruslan R Salakhutdinov, · 2094
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