Fetching the paper…
Reading the bibliography…
There is plenty of theoretical and empirical evidence that depth of neural networks is a crucial ingredient for their success.
A committee of neural networks for traffic sign classification
Ciresan, Dan, Meier, Ueli, Masci, Jonathan, and Schmidhuber, Jürgen · 1921
Earlier work this paper cites.
Computational limitations of small-depth circuits
Håstad, Johan · 1987
Earlier work this paper cites.
On the power of small-depth threshold circuits
Håstad, Johan and Goldmann, Mikael · 1991
Earlier work this paper cites.
Long short term memory
Hochreiter, Sepp and Schmidhuber, Jürgen · 1995
Earlier work this paper cites.
Learning to forget: Continual prediction with LSTM
Gers, Felix A., Schmidhuber, Jürgen, and Cummins, Fred · 1999
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, Xavier and Bengio, Yoshua · 2010
Earlier work this paper cites.
Multi-column deep neural networks for image classification
Ciresan, Dan, Meier, Ueli, and Schmidhuber, Jürgen · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
Cited alongside, same era.
Representation learning: A review and new perspectives
Bengio, Yoshua, Courville, Aaron, and Vincent, Pascal · 2013
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 · 2013
Cited alongside, same era.
On the number of linear regions of deep neural networks
Montufar, Guido F, Pascanu, Razvan, Cho, Kyunghyun, and Bengio, Yoshua · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, Karen and Zisserman, Andrew · 2014
Later among the works it cites.
Going deeper with convolutions
Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew · 2014
Later among the works it cites.
Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2015
Closest in time.
Deeply-supervised nets
Lee, Chen-Yu, Xie, Saining, Gallagher, Patrick, Zhang, Zhengyou, and Tu, Zhuowen · 2015
Closest in time.
Understanding locally competitive networks
Srivastava, Rupesh Kumar, Masci, Jonathan, Gomez, Faustino, and Schmidhuber, Jürgen · 2015
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Romero, Adriana, Ballas, Nicolas, Kahou, Samira Ebrahimi, Chassang, Antoine, Gatta, Carlo, and Bengio, Yoshua · 2014
Cited alongside, same era.
Flexible, high performance convolutional neural networks for image classification
Ciresan, DC, Meier, Ueli, Masci, Jonathan, Gambardella, Luca M, and Schmidhuber, Jürgen
Cited in the paper.
Closest in time.