Follow
Edward Meeds
Edward Meeds
Microsoft Research Cambridge
Verified email at microsoft.com
Title
Cited by
Cited by
Year
Soft weight-sharing for neural network compression
K Ullrich, E Meeds, M Welling
arXiv preprint arXiv:1702.04008, 2017
3482017
Modeling dyadic data with binary latent factors
E Meeds, Z Ghahramani, R Neal, S Roweis
Advances in neural information processing systems 19, 2006
2182006
An alternative infinite mixture of Gaussian process experts
E Meeds, S Osindero
Advances in neural information processing systems 18, 2005
1552005
Deterministic variational inference for robust bayesian neural networks
A Wu, S Nowozin, E Meeds, RE Turner, JM Hernandez-Lobato, AL Gaunt
arXiv preprint arXiv:1810.03958, 2018
1342018
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
E Meeds, M Welling
Uncertainty in Artificial Intelligence 30, 2014
1172014
Nonparametric bayesian biclustering
E Meeds, S Roweis
Technical report, University of Toronto, 2007
412007
Hamiltonian ABC
E Meeds, R Leenders, M Welling
Uncertainty in Artificial Intelligence 31, 2015
372015
MLitB: Machine Learning in the Browser
E Meeds, R Hendriks, S Al Faraby, M Bruntink, M Welling
PeerJ Computer Science 1, e11, 2015
322015
MLitB: Machine Learning in the Browser
E Meeds, R Hendriks, S al Faraby, M Bruntink, M Welling
http://arxiv.org/abs/1412.2432v1, 2014
322014
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
E Meeds, M Welling
Advances in Neural Information Processing Systems 28, 2015
312015
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
282008
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), 1-24, 2015
182015
Efficient amortised Bayesian inference for hierarchical and nonlinear dynamical systems
G Roeder, P Grant, A Phillips, N Dalchau, E Meeds
International Conference on Machine Learning, 4445-4455, 2019
152019
Automatic variational ABC
A Moreno, T Adel, E Meeds, JM Rehg, M Welling
arXiv preprint arXiv:1606.08549, 2016
132016
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
52014
Nonparametric Bayesian methods for extracting structure from data
E Meeds
University of Toronto, 2008
22008
Novelty detection model selection using volume estimation
E Meeds
UTML-TR-2005–004, Technical Report, University of Toronto, 2005
22005
Immunosequencing of the T-cell receptor repertoire reveals signatures specific for diagnosis and characterization of early Lyme disease
J Greissl, M Pesesky, SC Dalai, AW Rebman, MJ Soloski, EJ Horn, ...
medRxiv, 2021
12021
Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations
P Chapfuwa, S Rose, L Carin, E Meeds, R Henao
arXiv preprint arXiv:2202.12932, 2022
2022
Modelling ordinary differential equations using a variational auto encoder
E Meeds, G Roeder, N Dalchau
US Patent 11,030,275, 2021
2021
The system can't perform the operation now. Try again later.
Articles 1–20