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We develop unbiased implicit variational inference (UIVI), a method that expands the applicability of variational inference by defining an expressive variational family.
Exploiting tractable substructures in intractable networks
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Factorial hidden Markov models
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Approximating posterior distributions in belief networks using mixtures
Bishop, C. M., Lawrence, N. D., Jaakkola, T. S., and Jordan, M. I · 1998
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Variational Bayesian inference with stochastic search
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Stochastic variational inference
Hoffman, M. D., Blei, D. M., Wang, C., and Paisley, J · 2013
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Fixed-form variational posterior approximation through stochastic linear regression
Salimans, T. and Knowles, D. A · 2013
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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
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Black box variational inference
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Stochastic backpropagation and approximate inference in deep generative models
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Doubly stochastic variational Bayes for non-conjugate inference
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Automatic variational inference in Stan
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Variational inference with normalizing flows
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Covariances, robustness, and variational Bayes
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Variational inference using implicit distributions
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Automatic differentiation variational inference
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Filtering variational objectives
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Adversarial variational Bayes: Unifying variational autoencoders and generative adversarial networks
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Copula variational inference
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Boosting variational inference
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Masked autoregressive flow for density estimation
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Sticking the landing: Simple, lower-variance gradient estimators for variational inference
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Improving variational auto-encoders using Householder flow
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Hierarchical implicit models and likelihood-free variational inference
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Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms
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Advances in variational inference
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Implicit reparameterization gradients
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Variational rejection sampling
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Auto-encoding sequential Monte-Carlo
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