| Expectation propagation for approximate Bayesian inference TP Minka arXiv preprint arXiv:1301.2294, 2013 | 2420 | 2013 |
| A family of algorithms for approximate Bayesian inference TP Minka Massachusetts Institute of Technology, 2001 | 1325 | 2001 |
| Object categorization by learned universal visual dictionary J Winn, A Criminisi, T Minka Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 2 …, 2005 | 1189 | 2005 |
| TrueSkill™: a Bayesian skill rating system R Herbrich, T Minka, T Graepel Advances in neural information processing systems 19, 2006 | 1152 | 2006 |
| The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments IJ Cox, ML Miller, TP Minka, TV Papathomas, PN Yianilos IEEE transactions on image processing 9 (1), 20-37, 2000 | 1120 | 2000 |
| Estimating a Dirichlet distribution T Minka Technical report, MIT 1 (3), 4, 2000 | 1049 | 2000 |
| Automatic choice of dimensionality for PCA T Minka Advances in neural information processing systems 13, 2000 | 818 | 2000 |
| Divergence measures and message passing T Minka Technical report, Microsoft Research 17, 51, 2005 | 775 | 2005 |
| Expectation-propogation for the generative aspect model TP Minka, J Lafferty arXiv preprint arXiv:1301.0588, 2012 | 753 | 2012 |
| Cosegmentation of image pairs by histogram matching-incorporating a global constraint into mrfs C Rother, T Minka, A Blake, V Kolmogorov 2006 IEEE Computer Society Conference on Computer Vision and Pattern …, 2006 | 749 | 2006 |
| A useful distribution for fitting discrete data: revival of the Conway–Maxwell–Poisson distribution G Shmueli, TP Minka, JB Kadane, S Borle, P Boatwright Journal of the Royal Statistical Society Series C: Applied Statistics 54 (1 …, 2005 | 725 | 2005 |
| You are facing the Mona Lisa: Spot localization using PHY layer information S Sen, B Radunovic, RR Choudhury, T Minka Proceedings of the 10th international conference on Mobile systems …, 2012 | 662 | 2012 |
| maps: Draw geographical maps RA Becker, AR Wilks, R Brownrigg, TP Minka, A Deckmyn R package version 3 (0), 2018, 2018 | 655 | 2018 |
| Novelty and redundancy detection in adaptive filtering Y Zhang, J Callan, T Minka Proceedings of the 25th annual international ACM SIGIR conference on …, 2002 | 655 | 2002 |
| Interactive learning with a “society of models” TP Minka, RW Picard Pattern recognition 30 (4), 565-581, 1997 | 652 | 1997 |
| Bayesian color constancy revisited PV Gehler, C Rother, A Blake, T Minka, T Sharp 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008 | 553 | 2008 |
| Softrank: optimizing non-smooth rank metrics M Taylor, J Guiver, S Robertson, T Minka Proceedings of the 2008 International Conference on Web Search and Data …, 2008 | 483 | 2008 |
| Principled hybrids of generative and discriminative models JA Lasserre, CM Bishop, TP Minka 2006 IEEE Computer Society Conference on Computer Vision and Pattern …, 2006 | 452 | 2006 |
| A* sampling CJ Maddison, D Tarlow, T Minka Advances in neural information processing systems 27, 2014 | 436 | 2014 |
| Vision texture for annotation RW Picard, TP Minka Multimedia systems 3, 3-14, 1995 | 383 | 1995 |
John WinnPrincipal Researcher, Machine Learning Group, Microsoft Research CambridgeE-mail megerősítve itt: microsoft.com