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Deep generative models provide powerful tools for distributions over complicated manifolds, such as those of natural images.
Monte carlo methods of inference for implicit statistical models
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Bregman divergence as general framework to estimate unnormalized statistical models
Gutmann, M.U. and Hirayama, J · 2011
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Gutmann, Michael U. and Hyvarinen, Aapo · 2012
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Density ratio estimation in machine learning
Sugiyama, M., Suzuki, T., and Kanamori, T · 2012
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Auto-encoding variational bayes
Kingma, Diederik P and Welling, Max · 2013
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Generative adversarial nets
Goodfellow, Ian J., Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron C., and Bengio, Yoshua · 2014
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Likelihood-free inference via classification
Gutmann, M.U., Dutta, R., Kaski, S., and Corander, J · 2014
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Mode regularized generative adversarial networks
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Adversarial feature learning
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Adversarially learned inference
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