Edward Meeds

Publication Activity (10 Years)

Years Active: 2005-2023
Publications (10 Years): 10

Top Topics

Variational Inference

Top Venues

Publications

Melanie F. Pradier, Niranjani Prasad, Paidamoyo Chapfuwa, Sahra Ghalebikesabi, Max Ilse, Steven Woodhouse, Rebecca Elyanow, Javier Zazo, Javier Gonzalez, Julia Greissl, Edward Meeds
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires. CoRR (2023)
Melanie F. Pradier, Niranjani Prasad, Paidamoyo Chapfuwa, Sahra Ghalebikesabi, Maximilian Ilse, Steven Woodhouse, Rebecca Elyanow, Javier Zazo, Javier González Hernández, Julia Greissl, Edward Meeds
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires. MLHC (2023)
Paidamoyo Chapfuwa, Sherri Rose, Lawrence Carin, Edward Meeds, Ricardo Henao
Capturing actionable dynamics with structured latent ordinary differential equations. UAI (2022)
Paidamoyo Chapfuwa, Sherri Rose, Lawrence Carin, Edward Meeds, Ricardo Henao
Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations. CoRR (2022)
Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems. CoRR (2019)
Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt
Deterministic Variational Inference for Robust Bayesian Neural Networks. ICLR (2019)
Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems. ICML (2019)
Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt
Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks. CoRR (2018)
Karen Ullrich, Edward Meeds, Max Welling
Soft Weight-Sharing for Neural Network Compression. ICLR (Poster) (2017)
Karen Ullrich, Edward Meeds, Max Welling
Soft Weight-Sharing for Neural Network Compression. CoRR (2017)
Edward Meeds, Robert Leenders, Max Welling
Hamiltonian ABC. CoRR (2015)
Edward Meeds, Remco Hendriks, Said al Faraby, Magiel Bruntink, Max Welling
MLitB: machine learning in the browser. PeerJ Comput. Sci. 1 (2015)
Edward Meeds, Max Welling
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference. NIPS (2015)
Edward Meeds, Michael Chiang, Mary Lee, Olivier Cinquin, John S. Lowengrub, Max Welling
POPE: post optimization posterior evaluation of likelihood free models. BMC Bioinform. 16 (2015)
Edward Meeds, Robert Leenders, Max Welling
Hamiltonian ABC. UAI (2015)
Edward Meeds, Max Welling
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference. CoRR (2015)
Edward Meeds, Max Welling
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation. UAI (2014)
Edward Meeds, Max Welling
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation. CoRR (2014)
Edward Meeds, Remco Hendriks, Said al Faraby, Magiel Bruntink, Max Welling
MLitB: Machine Learning in the Browser. CoRR (2014)
Edward Meeds, David A. Ross, Richard S. Zemel, Sam T. Roweis
Learning stick-figure models using nonparametric Bayesian priors over trees. CVPR (2008)
Edward Meeds, Zoubin Ghahramani, Radford M. Neal, Sam T. Roweis
Modeling Dyadic Data with Binary Latent Factors. NIPS (2006)
Edward Meeds, Simon Osindero
An Alternative Infinite Mixture Of Gaussian Process Experts. NIPS (2005)