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Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems

May 28, 2019
Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds

* Accepted at ICML 2019 

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Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks

Oct 09, 2018
Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt

* 9 pages, 5 figures 

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Soft Weight-Sharing for Neural Network Compression

May 09, 2017
Karen Ullrich, Edward Meeds, Max Welling

* ICLR2017 

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Automatic Variational ABC

Jun 28, 2016
Alexander Moreno, Tameem Adel, Edward Meeds, James M. Rehg, Max Welling

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Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference

Dec 02, 2015
Edward Meeds, Max Welling

* NIPS 2015 camera ready 

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MLitB: Machine Learning in the Browser

Jun 17, 2015
Edward Meeds, Remco Hendriks, Said Al Faraby, Magiel Bruntink, Max Welling

* Revised for PeerJ Computer Science 

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Hamiltonian ABC

Mar 06, 2015
Edward Meeds, Robert Leenders, Max Welling

* Submission to UAI 2015 

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POPE: Post Optimization Posterior Evaluation of Likelihood Free Models

Dec 09, 2014
Edward Meeds, Michael Chiang, Mary Lee, Olivier Cinquin, John Lowengrub, Max Welling

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GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation

Jan 13, 2014
Edward Meeds, Max Welling

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