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Deep learning has achieved impressive results on many problems.
An empirical evaluation of deep architectures on problems with many factors of variation
Hugo Larochelle, Dumitru Erhan, Aaron Courville, James Bergstra, and Yoshua Bengio · 2007
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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.
Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
Cited alongside, same era.
Making a science of model search
James Bergstra, Dan Yamins, and David D. Cox · 2012
Cited alongside, same era.
SSD: single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott E. Reed, Cheng-Yang Fu, and Alexander C. Berg · 2015
Cited alongside, same era.
Cifar-10 (canadian institute for advanced research)
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton
Cited in the paper.
Taking the human out of the loop: A review of bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando de Freitas · 2015
Later among the works it cites.
Convolutional neural networks with low-rank regularization
Cheng Tai, Tong Xiao, Xiaogang Wang, and Weinan E · 2015
Later among the works it cites.
Efficient hyperparameter optimization and infinitely many armed bandits
Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2016
Later among the works it cites.
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