Edward Meeds
Edward Meeds
Microsoft Research Cambridge
Verified email at microsoft.com
TitleCited byYear
Modeling dyadic data with binary latent factors
E Meeds, Z Ghahramani, RM Neal, ST Roweis
Advances in neural information processing systems, 977-984, 2007
1992007
Soft weight-sharing for neural network compression
K Ullrich, E Meeds, M Welling
arXiv preprint arXiv:1702.04008, 2017
1372017
An alternative infinite mixture of Gaussian process experts
E Meeds, S Osindero
Advances in neural information processing systems, 883-890, 2006
1242006
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
E Meeds, M Welling
Uncertainty in Artificial Intelligence 30, 2014
742014
MLitB: Machine Learning in the Browser
E Meeds, R Hendriks, S Al Faraby, M Bruntink, M Welling
PeerJ Computer Science 1, e11, 2015
302015
MLitB: Machine Learning in the Browser
E Meeds, R Hendriks, S al Faraby, M Bruntink, M Welling
http://arxiv.org/abs/1412.2432v1, 2014
302014
Nonparametric bayesian biclustering
E Meeds, S Roweis
Technical report, University of Toronto, 2007
302007
Hamiltonian ABC
E Meeds, R Leenders, M Welling
Uncertainty in Artificial Intelligence 31, 2015
282015
Learning stick-figure models using nonparametric Bayesian priors over trees
EW Meeds, DA Ross, RS Zemel, ST Roweis
2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008
262008
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
E Meeds, M Welling
Advances in Neural Information Processing Systems 28, 2015
192015
Deterministic variational inference for robust bayesian neural networks
A Wu, S Nowozin, E Meeds, RE Turner, JM Hernández-Lobato, AL Gaunt
162018
Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation
M Chiang, A Cinquin, A Paz, E Meeds, CA Price, M Welling, O Cinquin
BMC biology 13 (1), 51, 2015
152015
Automatic variational ABC
A Moreno, T Adel, E Meeds, JM Rehg, M Welling
arXiv preprint arXiv:1606.08549, 2016
112016
Fixing variational Bayes: deterministic variational inference for Bayesian neural networks
A Wu, S Nowozin, E Meeds, RE Turner, JM Hernández-Lobato, AL Gaunt
arXiv preprint arXiv:1810.03958, 2018
72018
Bayesian inference with big data: a snapshot from a workshop
M Welling, YW Teh, C Andrieu, J Kominiarczuk, T Meeds, B Shahbaba, ...
ISBA Bulletin 21 (4), 8-11, 2014
42014
Nonparametric Bayesian methods for extracting structure from data
E Meeds
University of Toronto, 2008
12008
Novelty detection model selection using volume estimation
E Meeds
UTML-TR-2005-004, Technical Report, University of Toronto, 2005
12005
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
G Roeder, PK Grant, A Phillips, N Dalchau, E Meeds
arXiv preprint arXiv:1905.12090, 2019
2019
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
T Meeds, G Roeder, P Grant, A Phillips, N Dalchau
International Conference on Machine Learning, 4445-4455, 2019
2019
POPE: post optimization posterior evaluation of likelihood free models
E Meeds, M Chiang, M Lee, OC Cinquin, J Lowengrub, M Welling
BMC Bioinformatics 16 (264), 2015
2015
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Articles 1–20