| 1 Univariate Gaussians KP Murphy | | |
| A brief introduction to Bayes' Rule K Murphy | 7 | 2010 |
| A Brief Introduction to Graphical Models and Bayesian Networks2 K Murphy http://www. cs. ubc. ca/~ murphyk/Bayes/bnintro. html, 1998 | 340 | 1998 |
| A coupled HMM for audio-visual speech recognition AV Nefian, L Liang, X Pi, L Xiaoxiang, C Mao, K Murphy 2002 IEEE International Conference on Acoustics, Speech, and Signal …, 2002 | 326 | 2002 |
| A dynamic Bayesian network approach to figure tracking using learned dynamic models V Pavlovic, JM Rehg, TJ Cham, KP Murphy Proceedings of the seventh IEEE international conference on computer vision …, 1999 | 377 | 1999 |
| A hybrid conditional random field for estimating the underlying ground surface from airborne LiDAR data WL Lu, KP Murphy, JJ Little, A Sheffer, H Fu IEEE Transactions on Geoscience and Remote Sensing 47 (8), 2913-2922, 2009 | 91 | 2009 |
| A hybrid conditional random field for estimating the underlying ground surface from airborne LiDAR data. LWL Lu WeiLwun, KP Murphy, JJ Little, A Sheffer, FHB Fu HongBo | | 2009 |
| A non-myopic approach to visual search J Vogel, K Murphy Fourth Canadian Conference on Computer and Robot Vision (CRV'07), 227-234, 2007 | 21 | 2007 |
| A probabilistic perspective KP Murphy Text book, 2012 | 36 | 2012 |
| 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 | 1917 | 2015 |
| A stick-breaking likelihood for categorical data analysis with latent Gaussian models M Khan, S Mohamed, B Marlin, K Murphy Artificial Intelligence and Statistics, 610-618, 2012 | 59 | 2012 |
| A survey of POMDP solution techniques KP Murphy environment 2 (10), 2000 | 200 | 2000 |
| A tutorial on dynamic bayesian networks KP Murphy Internet, November, 2002 | 3 | 2002 |
| A variational approximation for Bayesian networks with discrete and continuous latent variables K Murphy arXiv preprint arXiv:1301.6724, 2013 | 146 | 2013 |
| A view of estimation of distribution algorithms through the lens of expectation-maximization D Brookes, A Busia, C Fannjiang, K Murphy, J Listgarten Proceedings of the 2020 Genetic and Evolutionary Computation Conference …, 2020 | 17 | 2020 |
| Accelerated training of conditional random fields with stochastic gradient methods SVN Vishwanathan, NN Schraudolph, MW Schmidt, KP Murphy Proceedings of the 23rd international conference on Machine learning, 969-976, 2006 | 415 | 2006 |
| Accelerating Bayesian structural inference for non-decomposable Gaussian graphical models B Moghaddam, E Khan, KP Murphy, BM Marlin Advances in Neural Information Processing Systems 22, 2009 | 33 | 2009 |
| Action localization in images and videos using relational features C Sun, A Shrivastava, CL Schmid, R Sukthankar, KP Murphy, ... US Patent 11,163,989, 2021 | 5 | 2021 |
| Active learning of causal Bayes net structure KP Murphy technical report, UC Berkeley, 2001 | 201 | 2001 |
| Actor-centric relation network C Sun, A Shrivastava, C Vondrick, K Murphy, R Sukthankar, C Schmid Proceedings of the European Conference on Computer Vision (ECCV), 318-334, 2018 | 237 | 2018 |