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We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
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Expectation propagation for approximate bayesian inference
Thomas P Minka · 2001
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Information theory and statistics: A tutorial
Imre Csiszár, Paul C Shields, et al · 2004
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Divergence measures and message passing
Tom Minka · 2005
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Probabilistic graphical models: principles and techniques
Daphne Koller and Nir Friedman · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Auto-encoding variational Bayes
Diederik Kingma and Max Welling · 2013
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Learning stochastic inverses
Andreas Stuhlmüller, Jacob Taylor, and Noah Goodman · 2013
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Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of cad models
Mathieu Aubry, Daniel Maturana, Alexei A Efros, Bryan C Russell, and Josef Sivic · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Stochastic expectation propagation
Yingzhen Li, José Miguel Hernández-Lobato, and Richard E Turner · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
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Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2015
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Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Unsupervised and semi-supervised learning with categorical generative adversarial networks
Jost Tobias Springenberg · 2015
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Unsupervised learning of video representations using LSTMs
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov · 2015
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TensorFlow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Temporal generative adversarial nets
Masaki Saito and Eiichi Matsumoto · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Improving generative adversarial networks with denoising feature matching
David Warde-Farley and Yoshua Bengio · 2016
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Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks
Tianfan Xue, Jiajun Wu, Katherine Bouman, and Bill Freeman · 2016
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InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Deep unsupervised clustering with Gaussian mixture variational autoencoders
Nat Dilokthanakul, Pedro AM Mediano, Marta Garnelo, Matthew CH Lee, Hugh Salimbeni, Kai Arulkumaran, and Murray Shanahan · 2016
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Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, and Aaron Courville · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Composing graphical models with neural networks for structured representations and fast inference
Matthew Johnson, David K Duvenaud, Alex Wiltschko, Ryan P Adams, and Sandeep R Datta · 2016
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Nal Kalchbrenner, Aaron van den Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, and Koray Kavukcuoglu · 2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Unsupervised learning of disentangled representations from video
Emily Denton and Vighnesh Birodkar · 2017
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Variational inference using implicit distributions
Ferenc Huszár · 2017
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Adversarial variational Bayes: Unifying variational autoencoders and generative adversarial networks
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
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Bayesian gan
Yunus Saatci and Andrew G Wilson · 2017
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Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2017
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Hierarchical implicit models and likelihood-free variational inference
Dustin Tran, Rajesh Ranganath, and David M Blei · 2017
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Mocogan: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2017
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Decomposing motion and content for natural video sequence prediction
Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, and Honglak Lee · 2017
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Variational message passing with structured inference networks
Wu Lin, Nicolas Hubacher, and Mohammad Emtiyaz Khan · 2018
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