| Machine learning, a probabilistic perspective C Robert CHANCE 27 (2), 62-63, 2014 | 6299* | 2014 |
| Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs LC Chen, G Papandreou, I Kokkinos, K Murphy, AL Yuille IEEE transactions on pattern analysis and machine intelligence 40 (4), 834-848, 2017 | 5723* | 2017 |
| Dynamic bayesian networks: representation, inference and learning KP Murphy, S Russell University of California, Berkeley, 2002 | 3129 | 2002 |
| LabelMe: a database and web-based tool for image annotation BC Russell, A Torralba, KP Murphy, WT Freeman International journal of computer vision 77 (1-3), 157-173, 2008 | 2734 | 2008 |
| Loopy belief propagation for approximate inference: An empirical study K Murphy, Y Weiss, MI Jordan arXiv preprint arXiv:1301.6725, 2013 | 1887 | 2013 |
| The bayes net toolbox for matlab K Murphy Computing science and statistics 33 (2), 1024-1034, 2001 | 1469 | 2001 |
| Rao-Blackwellised particle filtering for dynamic Bayesian networks A Doucet, N De Freitas, K Murphy, S Russell arXiv preprint arXiv:1301.3853, 2013 | 1413 | 2013 |
| Speed/accuracy trade-offs for modern convolutional object detectors J Huang, V Rathod, C Sun, M Zhu, A Korattikara, A Fathi, I Fischer, ... Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1125 | 2017 |
| Knowledge Vault: A Web-scale approach to probabilistic knowledge fusion XL Dong, K Murphy, E Gabrilovich, G Heitz, W Horn, N Lao, T Strohmann, ... KDD, 2014 | 1047 | 2014 |
| Context-based vision system for place and object recognition. A Torralba, KP Murphy, WT Freeman, MA Rubin ICCV 3, 273-280, 2003 | 1043 | 2003 |
| Contextual priming for object detection A Torralba International journal of computer vision 53 (2), 169-191, 2003 | 949 | 2003 |
| Sharing visual features for multiclass and multiview object detection A Torralba, KP Murphy, WT Freeman IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (5), 854-869, 2007 | 866 | 2007 |
| Learning the structure of dynamic probabilistic networks N Friedman, K Murphy, S Russell arXiv preprint arXiv:1301.7374, 2013 | 771 | 2013 |
| Sharing features: efficient boosting procedures for multiclass object detection A Torralba, KP Murphy, WT Freeman Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision …, 2004 | 767 | 2004 |
| A review of relational machine learning for knowledge graphs M Nickel, K Murphy, V Tresp, E Gabrilovich Proceedings of the IEEE 104 (1), 11-33, 2015 | 655 | 2015 |
| Modelling gene expression data using dynamic Bayesian networks K Murphy, S Mian Technical report, Computer Science Division, University of California …, 1999 | 646 | 1999 |
| Bayesian map learning in dynamic environments KP Murphy Advances in Neural Information Processing Systems, 1015-1021, 2000 | 629 | 2000 |
| Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation G Papandreou, LC Chen, KP Murphy, AL Yuille Proceedings of the IEEE international conference on computer vision, 1742-1750, 2015 | 605 | 2015 |
| Using the forest to see the trees: A graphical model relating features, objects, and scenes KP Murphy, A Torralba, WT Freeman Advances in neural information processing systems, 1499-1506, 2004 | 496 | 2004 |
| Contextual models for object detection using boosted random fields A Torralba, KP Murphy, WT Freeman Advances in neural information processing systems, 1401-1408, 2005 | 449 | 2005 |
