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Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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A kernel method for the two-sample-problem
Arthur Gretton, Karsten M Borgwardt, Malte Rasch, Bernhard Schölkopf, and Alex J Smola · 2007
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Variational recurrent auto-encoders
Otto Fabius and Joost R van Amersfoort · 2014
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Conditional generative adversarial nets for convolutional face generation
Jon Gauthier · 2014
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Generative adversarial networks
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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A test of relative similarity for model selection in generative models
Wacha Bounliphone, Eugene Belilovsky, Matthew B Blaschko, Ioannis Antonoglou, and Arthur Gretton · 2015
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A simple way to initialize recurrent networks of rectified linear units
Quoc V Le, Navdeep Jaitly, and Geoffrey E Hinton · 2015
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Generative moment matching networks
Yujia Li, Kevin Swersky, and Richard Zemel · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Cited alongside, same era.
A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Unitary evolution recurrent neural networks
Martin Arjovsky, Amar Shah, and Yoshua Bengio · 2016
Cited alongside, same era.
InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Generative models and model criticism via optimized maximum mean discrepancy
Dougal J Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alex Smola, and Arthur Gretton · 2016
Later among the works it cites.
On the quantitative analysis of Decoder-Based generative models
Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, and Roger Grosse · 2016
Later among the works it cites.
SeqGAN: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2016
Later among the works it cites.
Face aging with conditional generative adversarial networks
Grigory Antipov, Moez Baccouche, and Jean-Luc Dugelay · 2017
Closest in time.
Wasserstein GAN
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Privacy-preserving generative deep neural networks support clinical data sharing
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Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2016
Cited alongside, same era.
C-RNN-GAN: Continuous recurrent neural networks with adversarial training
Olof Mogren · 2016
Cited alongside, same era.
Pixel recurrent neural networks
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2016
Cited alongside, same era.
Generative adversarial text to image synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee · 2016
Cited alongside, same era.
Improved techniques for training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
Brett K. Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, and Casey S. Greene · 2017
Closest in time.
Generating multi-label discrete electronic health records using generative adversarial networks
Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F Stewart, and Jimeng Sun · 2017
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Improved training of wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
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Learning unitary operators with help from u (n)
Stephanie L Hyland and Gunnar Rätsch · 2017
Closest in time.
Adversarial learning for neural dialogue generation
Jiwei Li, Will Monroe, Tianlin Shi, Alan Ritter, and Dan Jurafsky · 2017
Closest in time.
Improving neural machine translation with conditional sequence generative adversarial nets
Zhen Yang, Wei Chen, Feng Wang, and Bo Xu · 2017
Closest in time.