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We extend the existing framework of semi-implicit variational inference (SIVI) and introduce doubly semi-implicit variational inference (DSIVI), a way to perform variational inference and learning when both the approximate posterior and the prior distribution are semi-implicit.
Bayesian learning for neural networks
R. M. Neal · 1995
Earlier work this paper cites.
Exploiting tractable substructures in intractable networks
L. K. Saul and M. I. Jordan · 1996
Earlier work this paper cites.
Improving the mean field approximation via the use of mixture distributions
T. S. Jaakkola and M. I. Jordan · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Sparse Bayesian Learning and the Relevance Vector Machine
M. Tipping · 2000
Earlier work this paper cites.
On the quantitative analysis of deep belief networks
R. Salakhutdinov and I. Murray · 2008
Earlier work this paper cites.
Estimating divergence functionals and the likelihood ratio by convex risk minimization
X. Nguyen, M. J. Wainwright, and M. I. Jordan · 2010
Earlier work this paper cites.
Density ratio estimation in machine learning
M. Sugiyama, T. Suzuki, and T. Kanamori · 2012
Earlier work this paper cites.
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D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Doubly stochastic variational bayes for non-conjugate inference
M. Titsias and M. Lázaro-Gredilla · 2014
Earlier work this paper cites.
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Y. Burda, R. Grosse, and R. Salakhutdinov · 2015
Earlier work this paper cites.
Linear response methods for accurate covariance estimates from mean field variational bayes
R. J. Giordano, T. Broderick, and M. I. Jordan · 2015
Earlier work this paper cites.
Probabilistic backpropagation for scalable learning of bayesian neural networks
J. M. Hernández-Lobato and R. Adams · 2015
Earlier work this paper cites.
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