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We propose a novel approximate inference algorithm that approximates a target distribution by amortising the dynamics of a user-selected MCMC sampler.
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Yarin Gal and Zoubin Ghahramani · 2016
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Improving variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, and Max Welling · 2016
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Wild variational approximations
Yingzhen Li and Qiang Liu · 2016
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Rényi divergence variational inference
Yingzhen Li and Richard E Turner · 2016
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A kernelized stein discrepancy for goodness-of-fit tests and model evaluation
Qiang Liu, Jason D Lee, and Michael I Jordan · 2016
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
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Li Wan, Matthew Zeiler, Sixin Zhang, Yann L Cun, and Rob Fergus · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Diederik P Kingma and Max Welling · 2014
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Rajesh Ranganath, Sean Gerrish, and David M Blei · 2014
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Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Neural adaptive sequential monte carlo
Shixiang Gu, Zoubin Ghahramani, and Richard E Turner · 2015
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Early stopping is nonparametric variational inference
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A variational analysis of stochastic gradient algorithms
Stephan Mandt, Matthew D Hoffman, and David M Blei · 2016
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Learning in implicit generative models
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Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Lucas Theis, Aaron van den Oord, and Matthias Bethge · 2016
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Martin Arjovsky, Soumith Chintala, and Leon Bottou · 2017
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