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It is widely believed that learning good representations is one of the main reasons for the success of deep neural networks.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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.
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Yann N Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Cifar-10 (canadian institute for advanced research)
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton
Cited in the paper.
Convergent learning: Do different neural networks learn the same representations?
Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, and John Hopcroft · 2016
Later among the works it cites.
Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein · 2017
Later among the works it cites.
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