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Variational inference is a powerful tool for approximate inference, and it has been recently applied for representation learning with deep generative models.
A stochastic approximation method
Robbins, Herbert and Monro, S · 1951
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Learning stochastic feedforward networks
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Keeping the neural networks simple by minimizing the description length of the weights
Hinton, G. and Van Camp, D · 1993
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Autoencoders, minimum description length, and helmholtz free energy
Hinton, Geoffrey E and Zemel, Richard S · 1994
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Exploiting tractable substructures in intractable networks
Saul, Lawrence K and Jordan, Michael I · 1996
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Bayesian methods for mixtures of experts
Waterhouse, S., MacKay, D., and Robinson, T · 1996
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Approximating posterior distributions in belief networks using mixtures
Bishop, Christopher M., Lawrence, Neil D., Jordan, Michael I., and Jaakkola, Tommi · 1998
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Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
Gelman, Andrew and Meng, Xiao-Li · 1998
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Improving the mean field approximation via the use of mixture distributions
Jaakkola, Tommi S and Jordan, Michael I · 1998
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Annealed importance sampling
Neal, Radford M · 1998
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An introduction to variational methods for graphical models
Jordan, Michael I, Ghahramani, Zoubin, Jaakkola, Tommi S, and Saul, Lawrence K · 1999
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Probabilistic principal component analysis
Tipping, Michael E and Bishop, Christopher M · 1999
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Variational Inference in Probabilistic Models
Lawrence, Neil · 2000
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A sequential particle filter method for static models
Chopin, Nicolas · 2002
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An auxiliary variational method
Agakov, Felix V and Barber, David · 2004
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Probabilistic non-linear principal component analysis with Gaussian process latent variable models
Lawrence, Neil · 2005
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A unifying view of sparse approximate Gaussian process regression
Quiñonero-Candela, Joaquin and Rasmussen, Carl Edward · 2005
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A fast learning algorithm for deep belief nets
Hinton, Geoffrey E, Osindero, Simon, and Teh, Yee-Whye · 2006
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An Introduction to Copulas (Springer Series in Statistics)
Nelsen, Roger B · 2006
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Gaussian processes for machine learning
Rasmussen, Carl Edward and Williams, Christopher K I · 2006
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Fast Gaussian process methods for point process intensity estimation
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Stochastic variational inference
Hoffman, Matthew D, Blei, David M, Wang, Chong, and Paisley, John · 2013
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Deep autoregressive networks
Gregor, Karol, Danihelka, Ivo, Mnih, Andriy, Blundell, Charles, and Wierstra, Daan · 2014
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Auto-encoding variational Bayes
Kingma, Diederik P and Welling, Max · 2014
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Iterative neural autoregressive distribution estimator nade-k
Raiko, Tapani, Li, Yao, Cho, Kyunghyun, and Bengio, Yoshua · 2014
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Black box variational inference
Ranganath, Rajesh, Gerrish, Sean, and Blei, David M · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, Danilo Jimenez, Mohamed, Shakir, and Wierstra, Daan · 2014
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