| 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 | 17386* | 2017 |
| Machine learning, a probabilistic perspective C Robert CHANCE 27 (2), 62-63, 2014 | 13453* | 2014 |
| Dynamic bayesian networks: representation, inference and learning KP Murphy University of California, Berkeley, 2002 | 3747 | 2002 |
| 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 | 2934 | 2017 |
| Loopy belief propagation for approximate inference: An empirical study K Murphy, Y Weiss, MI Jordan arXiv preprint arXiv:1301.6725, 2013 | 2254 | 2013 |
| 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 | 1949 | 2014 |
| Progressive neural architecture search C Liu, B Zoph, M Neumann, J Shlens, W Hua, LJ Li, L Fei-Fei, A Yuille, ... Proceedings of the European conference on computer vision (ECCV), 19-34, 2018 | 1867 | 2018 |
| The bayes net toolbox for matlab K Murphy Computing science and statistics 33 (2), 1024-1034, 2001 | 1686 | 2001 |
| Rao-Blackwellised particle filtering for dynamic Bayesian networks A Doucet, N De Freitas, K Murphy, S Russell arXiv preprint arXiv:1301.3853, 2013 | 1680 | 2013 |
| 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 | 1598 | 2015 |
| 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 | 1336 | 2015 |
| Context-based vision system for place and object recognition A Torralba, KP Murphy, WT Freeman, MA Rubin Computer Vision, IEEE International Conference on 2, 273-273, 2003 | 1255 | 2003 |
| Deep variational information bottleneck AA Alemi, I Fischer, JV Dillon, K Murphy arXiv preprint arXiv:1612.00410, 2016 | 1202 | 2016 |
| Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification S Xie, C Sun, J Huang, Z Tu, K Murphy Proceedings of the European conference on computer vision (ECCV), 305-321, 2018 | 1079 | 2018 |
| 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 | 966 | 2007 |
| Videobert: A joint model for video and language representation learning C Sun, A Myers, C Vondrick, K Murphy, C Schmid Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 916 | 2019 |
| Learning the structure of dynamic probabilistic networks N Friedman, K Murphy, S Russell arXiv preprint arXiv:1301.7374, 2013 | 874 | 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 | 843 | 2004 |
| Towards accurate multi-person pose estimation in the wild G Papandreou, T Zhu, N Kanazawa, A Toshev, J Tompson, C Bregler, ... Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 824 | 2017 |
| Naive bayes classifiers KP Murphy University of British Columbia 18 (60), 1-8, 2006 | 796 | 2006 |
