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In this work, we investigate a novel training procedure to learn a generative model as the transition operator of a Markov chain, such that, when applied repeatedly on an unstructured random noise sample, it will denoise it into a sample that matches the target distribution from the training set.
The mnist database of handwritten digits, 1998
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Alex. Krizhevsky and Geoffrey E Hinton · 2009
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Ruslan Salakhutdinov and Geoffrey E Hinton · 2009
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The toronto face database
Josh M Susskind, Adam K Anderson, and Geoffrey E Hinton · 2010
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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
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Hugo Larochelle and Iain Murray · 2011
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A generative process for sampling contractive auto-encoders
Salah Rifai, Yoshua Bengio, Yann Dauphin, and Pascal Vincent · 2012
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Better mixing via deep representations
Yoshua Bengio, Grégoire Mesnil, Yann Dauphin, and Salah Rifai · 2013
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Deep generative stochastic networks trainable by backprop
Yoshua Bengio, Eric Laufer, Guillaume Alain, and Jason Yosinski · 2014
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Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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GSNs: generative stochastic networks
Guillaume Alain, Yoshua Bengio, Li Yao, Jason Yosinski, Eric Thibodeau-Laufer, Saizheng Zhang, and Pascal Vincent · 2016
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A note on the evaluation of generative models
Aäron van den Oord Lucas Theis and Matthias Bethge · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
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Improved techniques for training gans
Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Sergey Ioffe and Christian Szegedy · 2015
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Yujia Li, Kevin Swersky, and Richard Zemel · 2015
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Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Markov chain monte carlo and variational inference: Bridging the gap
Tim Salimans, Diederik Kingma, and Max Welling · 2015
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Theano Development Team · 2016
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Pixel recurrent neural networks
Aäron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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On the quantitative analysis of decoder-based generative models
Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, and Roger B. Grosse · 2016
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The variational walkback algorithm
Anirudh Goyal, Nan Rosemary Ke, Alex Lamb, and Yoshua Bengio · 2017
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