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Edward Meeds
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2020 – today
- 2023
- [c11]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: 588-611 - [i9]Melanie F. Pradier, Niranjani Prasad, Paidamoyo Chapfuwa, Sahra Ghalebikesabi, Max Ilse, Steven Woodhouse, Rebecca Elyanow, Javier Zazo, Javier González Hernández, Julia Greissl, Edward Meeds:
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires. CoRR abs/2304.13737 (2023) - 2022
- [c10]Paidamoyo Chapfuwa, Sherri Rose, Lawrence Carin, Edward Meeds, Ricardo Henao:
Capturing actionable dynamics with structured latent ordinary differential equations. UAI 2022: 286-295 - [i8]Paidamoyo Chapfuwa, Sherri Rose, Lawrence Carin, Edward Meeds, Ricardo Henao:
Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations. CoRR abs/2202.12932 (2022)
2010 – 2019
- 2019
- [c9]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 - [c8]Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds:
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems. ICML 2019: 4445-4455 - [i7]Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds:
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems. CoRR abs/1905.12090 (2019) - 2018
- [i6]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 abs/1810.03958 (2018) - 2017
- [c7]Karen Ullrich, Edward Meeds, Max Welling:
Soft Weight-Sharing for Neural Network Compression. ICLR (Poster) 2017 - [i5]Karen Ullrich, Edward Meeds, Max Welling:
Soft Weight-Sharing for Neural Network Compression. CoRR abs/1702.04008 (2017) - 2015
- [j2]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: 264:1-264:20 (2015) - [j1]Edward Meeds, Remco Hendriks, Said al Faraby
MLitB: machine learning in the browser. PeerJ Comput. Sci. 1: e11 (2015) - [c6]Edward Meeds, Max Welling:
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference. NIPS 2015: 2080-2088 - [c5]Edward Meeds, Robert Leenders, Max Welling:
Hamiltonian ABC. UAI 2015: 582-591 - [i4]Edward Meeds, Robert Leenders, Max Welling:
Hamiltonian ABC. CoRR abs/1503.01916 (2015) - [i3]Edward Meeds, Max Welling:
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference. CoRR abs/1506.03693 (2015) - 2014
- [c4]Edward Meeds, Max Welling:
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation. UAI 2014: 593-602 - [i2]Edward Meeds, Max Welling:
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation. CoRR abs/1401.2838 (2014) - [i1]Edward Meeds, Remco Hendriks, Said al Faraby, Magiel Bruntink, Max Welling:
MLitB: Machine Learning in the Browser. CoRR abs/1412.2432 (2014)
2000 – 2009
- 2008
- [b1]Edward Meeds:
Nonparametric Bayesian Methods for Extracting Structure from Data. University of Toronto, Canada, 2008 - [c3]Edward Meeds, David A. Ross, Richard S. Zemel, Sam T. Roweis:
Learning stick-figure models using nonparametric Bayesian priors over trees. CVPR 2008 - 2006
- [c2]Edward Meeds, Zoubin Ghahramani, Radford M. Neal, Sam T. Roweis:
Modeling Dyadic Data with Binary Latent Factors. NIPS 2006: 977-984 - 2005
- [c1]Edward Meeds, Simon Osindero:
An Alternative Infinite Mixture Of Gaussian Process Experts. NIPS 2005: 883-890
Coauthor Index
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