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Recent research in the deep learning field has produced a plethora of new architectures.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 1958
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The timeless way of building , volume 1
Christopher Alexander · 1979
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Design patterns: elements of reusable object-oriented software
Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides · 1995
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Boosting neural networks
Holger Schwenk and Yoshua Bengio · 2000
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The elements of statistical learning , volume 1
Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2001
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Neural networks: tricks of the trade
Genevieve B Orr and Klaus-Robert Müller · 2003
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Deep learning made easier by linear transformations in perceptrons
Tapani Raiko, Harri Valpola, and Yann LeCun · 2012
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Min Lin, Qiang Chen, and Shuicheng Yan · 2013
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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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On the computational efficiency of training neural networks
Roi Livni, Shai Shalev-Shwartz, and Ohad Shamir · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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The unreasonable effectiveness of noisy data for fine-grained recognition
Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, and Li Fei-Fei · 2015
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Competitive multi-scale convolution
Zhibin Liao and Gustavo Carneiro · 2015
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Bilinear cnn models for fine-grained visual recognition
Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji · 2015
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Deconstructing the ladder network architecture
Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron Courville, and Yoshua Bengio · 2015
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Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 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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Zoneout: Regularizing rnns by randomly preserving hidden activations
David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Aaron Courville, et al · 2016
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Fractalnet: Ultra-deep neural networks without residuals
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2016
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Qianli Liao, Kenji Kawaguchi, and Tomaso Poggio · 2016
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Convolutional residual memory networks
Joel Moniz and Christopher Pal · 2016
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Leslie N Smith, Emily M Hand, and Timothy Doster · 2015
Cited alongside, same era.
Training very deep networks
Rupesh K Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
Cited alongside, same era.
Masoud Abdi and Saeid Nahavandi · 2016
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolution
François Chollet · 2016
Cited alongside, same era.
Caglar Gulcehre, Marcin Moczulski, Misha Denil, and Yoshua Bengio · 2016
Cited alongside, same era.
Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, and Christopher Ré · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Diederik P Kingma · 2016
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Shreyas Saxena and Jakob Verbeek · 2016
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Weighted residuals for very deep networks
Falong Shen and Gang Zeng · 2016
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Swapout: Learning an ensemble of deep architectures
Saurabh Singh, Derek Hoiem, and David Forsyth · 2016
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Gradual dropin of layers to train very deep neural networks
Leslie N Smith, Emily M Hand, and Timothy Doster · 2016
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, and Vincent Vanhoucke · 2016
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Resnet in resnet: Generalizing residual architectures
Sasha Targ, Diogo Almeida, and Kevin Lyman · 2016
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Residual networks are exponential ensembles of relatively shallow networks
Andreas Veit, Michael Wilber, and Serge Belongie · 2016
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Jingdong Wang, Zhen Wei, Ting Zhang, and Wenjun Zeng · 2016
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Understanding data augmentation for classification: when to warp?
Sebastien C Wong, Adam Gatt, Victor Stamatescu, and Mark D McDonnell · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Residual networks of residual networks: Multilevel residual networks
Ke Zhang, Miao Sun, Tony X Han, Xingfang Yuan, Liru Guo, and Tao Liu · 2016
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