| Soft weight-sharing for neural network compression K Ullrich, E Meeds, M Welling arXiv preprint arXiv:1702.04008, 2017 | 237 | 2017 |
| Modeling dyadic data with binary latent factors E Meeds, Z Ghahramani, RM Neal, ST Roweis Advances in neural information processing systems 19, 977, 2007 | 218 | 2007 |
| An alternative infinite mixture of Gaussian process experts E Meeds, S Osindero Advances in neural information processing systems 18, 883, 2006 | 140 | 2006 |
| GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation E Meeds, M Welling Uncertainty in Artificial Intelligence 30, 2014 | 96 | 2014 |
| 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 | 70 | 2018 |
| Hamiltonian ABC E Meeds, R Leenders, M Welling Uncertainty in Artificial Intelligence 31, 2015 | 35 | 2015 |
| Nonparametric bayesian biclustering E Meeds, S Roweis Technical report, University of Toronto, 2007 | 33 | 2007 |
| MLitB: Machine Learning in the Browser E Meeds, R Hendriks, S Al Faraby, M Bruntink, M Welling PeerJ Computer Science 1, e11, 2015 | 31 | 2015 |
| MLitB: Machine Learning in the Browser E Meeds, R Hendriks, S al Faraby, M Bruntink, M Welling http://arxiv.org/abs/1412.2432v1, 2014 | 31 | 2014 |
| 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 | 28 | 2008 |
| Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference E Meeds, M Welling Advances in Neural Information Processing Systems 28, 2015 | 26 | 2015 |
| 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 | 17 | 2015 |
| Automatic variational ABC A Moreno, T Adel, E Meeds, JM Rehg, M Welling arXiv preprint arXiv:1606.08549, 2016 | 12 | 2016 |
| 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 | 9 | 2019 |
| 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 | 5 | 2014 |
| Novelty detection model selection using volume estimation E Meeds UTML-TR-2005–004, Technical Report, University of Toronto, 2005 | 2 | 2005 |
| Nonparametric Bayesian methods for extracting structure from data E Meeds University of Toronto, 2008 | 1 | 2008 |
| 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 |
Max WellingProfessor Machine Learning, University of AmsterdamVerified email at uva.nl
