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Generative models for deep learning are promising both to improve understanding of the model, and yield training methods requiring fewer labeled samples.
Unsupervised learning of distributions on binary vectors using two layer networks
Yoav Freund and David Haussler · 1994
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes
Andrew Y. Ng and Michael I. Jordan · 2001
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Greedy layer-wise training of deep networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle · 2006
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Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
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A fast learning algorithm for deep belief nets
Geoffrey E. Hinton, Simon Osindero, and Yee Whye Teh · 2006
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Learning deep architectures for AI
Yoshua Bengio · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Lecutre notes on basic tools from empirical processes theory applied to compress sensing problem
Guillaume Lecue · 2009
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoff Hinton · 2012
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Provable bounds for learning some deep representations
Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Semi-supervised learning with deep generative models
Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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Understanding deep image representations by inverting them
A. Mahendran and A. Vedaldi · 2015
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Path-sgd: Path-normalized optimization in deep neural networks
Behnam Neyshabur, Ruslan Salakhutdinov, and Nathan Srebro · 2015
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A Probabilistic Theory of Deep Learning
A. B. Patel, T. Nguyen, and R. G. Baraniuk · 2015
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Deep exponential families
Rajesh Ranganath, Linpeng Tang, Laurent Charlin, and David M. Blei · 2015
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Sanjeev Arora, Aditya Bhaskara, Rong Ge, and Tengyu Ma · 2014
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Deep generative stochastic networks trainable by backprop
Yoshua Bengio, Eric Thibodeau-Laufer, Guillaume Alain, and Jason Yosinski
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Generalized denoising auto-encoders as generative models
Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y Ng
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Deep Learning and the Information Bottleneck Principle
N. Tishby and N. Zaslavsky · 2015
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