Edward Meeds
Edward Meeds
Microsoft Research Cambridge
Geverifieerd e-mailadres voor microsoft.com
Titel
Geciteerd door
Geciteerd door
Jaar
Soft weight-sharing for neural network compression
K Ullrich, E Meeds, M Welling
arXiv preprint arXiv:1702.04008, 2017
2542017
Modeling dyadic data with binary latent factors
E Meeds, Z Ghahramani, RM Neal, ST Roweis
Advances in neural information processing systems 19, 977, 2007
2212007
An alternative infinite mixture of Gaussian process experts
E Meeds, S Osindero
Advances in neural information processing systems 18, 883, 2006
1442006
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
E Meeds, M Welling
Uncertainty in Artificial Intelligence 30, 2014
1012014
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
792018
Hamiltonian ABC
E Meeds, R Leenders, M Welling
Uncertainty in Artificial Intelligence 31, 2015
362015
Nonparametric bayesian biclustering
E Meeds, S Roweis
Technical report, University of Toronto, 2007
342007
MLitB: Machine Learning in the Browser
E Meeds, R Hendriks, S Al Faraby, M Bruntink, M Welling
PeerJ Computer Science 1, e11, 2015
312015
MLitB: Machine Learning in the Browser
E Meeds, R Hendriks, S al Faraby, M Bruntink, M Welling
http://arxiv.org/abs/1412.2432v1, 2014
312014
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
E Meeds, M Welling
Advances in Neural Information Processing Systems 28, 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
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
172015
Automatic variational ABC
A Moreno, T Adel, E Meeds, JM Rehg, M Welling
arXiv preprint arXiv:1606.08549, 2016
122016
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
102019
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
Novelty detection model selection using volume estimation
E Meeds
UTML-TR-2005–004, Technical Report, University of Toronto, 2005
22005
Nonparametric Bayesian methods for extracting structure from data
E Meeds
University of Toronto, 2008
12008
Modelling ordinary differential equations using a variational auto encoder
E Meeds, G Roeder, N Dalchau
US Patent App. 16/255,778, 2020
2020
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
POPE: Post Optimization Posterior Evaluation of Likelihood Free Models
E Meeds, M Chiang, M Lee, O Cinquin, J Lowengrub, M Welling
http://arxiv.org/pdf/1412.3051v1.pdf, 2014
2014
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Artikelen 1–20