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Layer-sequential unit-variance (LSUV) initialization - a simple method for weight initialization for deep net learning - is proposed.
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Greedy layer-wise training of deep networks
Bengio, Yoshua, Lamblin, Pascal, Popovici, Dan, and Larochelle, Hugo · 2007
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, Alex · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, Xavier and Bengio, Yoshua · 2010
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Deep sparse rectifier neural networks
Glorot, Xavier, Bordes, Antoine, and Bengio, Yoshua · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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Lecture 6.5: RMSProp – Divide the gradient by a running average of its recent magnitude
Tieleman, Tijmen and Hinton, Geoffrey · 2012
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Maxout networks
Goodfellow, Ian J., Warde-Farley, David, Mirza, Mehdi, Courville, Aaron C., and Bengio, Yoshua · 2013
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Rectifier nonlinearities improve neural network acoustic models
Maas, Andrew L, Hannun, Awni Y, and Ng, Andrew Y · 2013
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Distilling the Knowledge in a Neural Network
Hinton, Geoffrey, Vinyals, Oriol, and Dean, Jeff · 2014
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Caffe: Convolutional architecture for fast feature embedding
Jia, Yangqing, Shelhamer, Evan, Donahue, Jeff, Karayev, Sergey, Long, Jonathan, Girshick, Ross, Guadarrama, Sergio, and Darrell, Trevor · 2014
Cited alongside, same era.
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Saxe, Andrew M., McClelland, James L., and Ganguli, Surya · 2014
Cited alongside, same era.
Striving for Simplicity: The All Convolutional Net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2014
Cited alongside, same era.
Random Walk Initialization for Training Very Deep Feedforward Networks
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
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Deeply-supervised nets
Lee, Chen-Yu, Xie, Saining, Gallagher, Patrick W., Zhang, Zhengyou, and Tu, Zhuowen · 2015
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Fitnets: Hints for thin deep nets
Romero, Adriana, Ballas, Nicolas, Kahou, Samira Ebrahimi, Chassang, Antoine, Gatta, Carlo, and Bengio, Yoshua · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng, Karpathy, Andrej, Khosla, Aditya, Bernstein, Michael, Berg, Alexander C., and Fei-Fei, Li · 2015
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Very Deep Multilingual Convolutional Neural Networks for LVCSR
Sercu, T., Puhrsch, C., Kingsbury, B., and LeCun, Y · 2015
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Sussillo, David and Abbott, L. F · 2014
Cited alongside, same era.
Batch-normalized Maxout Network in Network
Chang, J.-R. and Chen, Y.-S · 2015
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Classifying plankton with deep neural networks, 2015
Dieleman, Sander · 2015
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2015
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Graham, Ben
Cited in the paper.
Train you very own deep convolutional network, 2014b
Graham, Ben
Cited in the paper.
Spatially-sparse convolutional neural networks
Graham, Ben
Cited in the paper.
Simonyan, Karen and Zisserman, Andrew · 2015
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Srivastava, Rupesh Kumar, Greff, Klaus, and Schmidhuber, Jürgen · 2015
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Going deeper with convolutions
Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew · 2015
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