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We introduce the Metropolis-Hastings generative adversarial network (MH-GAN), which combines aspects of Markov chain Monte Carlo and GANs.
Markov chains for exploring posterior distributions
Tierney, L · 1994
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Versions of the sign test in the presence of ties
Coakley, C. W. and Heise, M. A · 1996
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Introducing Markov chain Monte Carlo
Gilks, W. R., Richardson, S., and Spiegelhalter, D. J · 1996
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Prequential analysis
Dawid, A. P · 1997
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Possible biases induced by MCMC convergence diagnostics
Cowles, M. K., Roberts, G. O., and Rosenthal, J. S · 1999
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Notes on the KL-divergence between a Markov chain and its equilibrium distribution
Murray, I. and Salakhutdinov, R · 2008
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80 million tiny images: A large data set for nonparametric object and scene recognition
Torralba, A., Fergus, R., and Freeman, W. T · 2008
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Density Ratio Estimation in Machine Learning
Sugiyama, M., Suzuki, T., and Kanamori, T · 2012
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Cited alongside, same era.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
Cited alongside, same era.
Density estimation using real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
Cited alongside, same era.
Adversarially learned inference
Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., and Courville, A · 2016
Cited alongside, same era.
Progressive growing of GANs for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2017
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Adversarial sequential Monte Carlo
Kempinska, K. and Shawe-Taylor, J · 2017
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Beta calibration: A well-founded and easily implemented improvement on logistic calibration for binary classifiers
Kull, M., Filho, T. S., and Flach, P · 2017
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Understanding generative adversarial networks
Shibuya, N · 2017
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A-NICE-MC: Adversarial training for MCMC
Song, J., Zhao, S., and Ermon, S · 2017
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Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X., and Chen, X · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A · 2017
Cited alongside, same era.
Azadi, S., Olsson, C., Darrell, T., Goodfellow, I., and Odena, A · 2018
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Metropolis-Hastings view on variational inference and adversarial training
Neklyudov, K., Shvechikov, P., and Vetrov, D · 2018
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