Fetching the paper…
Reading the bibliography…
Convolutional neural networks perform well on object recognition because of a number of recent advances: rectified linear units (ReLUs), data augmentation, dropout, and large labelled datasets.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
Geoffrey Hinton, Simon Osindero, and Yee-Whye Teh · 2006
Earlier work this paper cites.
Greedy layer-wise training of deep networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, Hugo Larochelle, et al · 2007
Earlier work this paper cites.
What is the best multi-stage architecture for object recognition?
Kevin Jarrett, Koray Kavukcuoglu, M Ranzato, and Yann LeCun · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y Ng · 2009
Earlier work this paper cites.
Theano: a CPU and GPU math expression compiler
James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio · 2010
Earlier work this paper cites.
Why does unsupervised pre-training help deep learning?
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio · 2010
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
Earlier work this paper cites.
Deconvolutional networks
Matthew D Zeiler, Dilip Krishnan, Graham W Taylor, and Robert Fergus · 2010
Cited alongside, same era.
Selecting receptive fields in deep networks
Adam Coates and Andrew Y Ng · 2011
Cited alongside, same era.
An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Y Ng, and Honglak Lee · 2011
Cited alongside, same era.
Stacked convolutional auto-encoders for hierarchical feature extraction
Jonathan Masci, Ueli Meier, Dan Cireşan, and Jürgen Schmidhuber · 2011
Cited alongside, same era.
Contractive auto-encoders: Explicit invariance during feature extraction
Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio · 2011
Cited alongside, same era.
Adaptive deconvolutional networks for mid and high level feature learning
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2013
Later among the works it cites.
Min Lin, Qiang Chen, and Shuicheng Yan · 2013
Later among the works it cites.
Multi-task bayesian optimization
Kevin Swersky, Jasper Snoek, and Ryan P Adams · 2013
Later among the works it cites.
Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2014
Closest in time.
Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, and Zhuowen Tu · 2014
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Matthew D Zeiler, Graham W Taylor, and Rob Fergus · 2011
Cited alongside, same era.
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Cited alongside, same era.
Unsupervised feature learning for rgb-d based object recognition
Liefeng Bo, Xiaofeng Ren, and Dieter Fox · 2013
Cited alongside, same era.
Pylearn2: a machine learning research library
Ian J Goodfellow, David Warde-Farley, Pascal Lamblin, Vincent Dumoulin, Mehdi Mirza, Razvan Pascanu, James Bergstra, Frédéric Bastien, and Yoshua Bengio
Cited in the paper.
Ian J Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and Yoshua Bengio
Cited in the paper.
Stable and efficient representation learning with nonnegativity constraints
Tsung-Han Lin and H. T. Kung · 2014
Closest in time.
Convolutional kernel networks
Julien Mairal, Piotr Koniusz, Zaid Harchaoui, and Cordelia Schmid · 2014
Closest in time.
Zero-bias autoencoders and the benefits of co-adapting features
Roland Memisevic, Kishore Konda, and David Krueger · 2014
Closest in time.