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Variational Autoencoders (VAEs) are expressive latent variable models that can be used to learn complex probability distributions from training data.
A general class of coefficients of divergence of one distribution from another
Ali, Syed Mumtaz and Silvey, Samuel D · 1966
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Multilayer feedforward networks are universal approximators
Hornik, Kurt, Stinchcombe, Maxwell, and White, Halbert · 1989
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Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
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The elements of statistical learning , volume 1
Friedman, Jerome, Hastie, Trevor, and Tibshirani, Robert · 2001
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Annealed importance sampling
Neal, Radford M · 2001
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Estimating divergence functionals and the likelihood ratio by convex risk minimization
Nguyen, XuanLong, Wainwright, Martin J, and Jordan, Michael I · 2010
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Convex analysis and minimization algorithms I: fundamentals , volume 305
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Kingma, Diederik P and Welling, Max · 2013
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Bayesian data analysis , volume 2
Gelman, Andrew, Carlin, John B, Stern, Hal S, and Rubin, Donald B · 2014
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Generative adversarial nets
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
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Li, Yingzhen and Liu, Qiang · 2016
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Stan modeling language users guide and reference manual, Version 2.14.0, 2016
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