| Learning to track and identify players from broadcast sports videos WL Lu, JA Ting, JJ Little, KP Murphy IEEE transactions on pattern analysis and machine intelligence 35 (7), 1704-1716, 2013 | 327 | 2013 |
| A Kalman filter for robust outlier detection JA Ting, E Theodorou, S Schaal 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2007 | 212 | 2007 |
| Identifying players in broadcast sports videos using conditional random fields WL Lu, JA Ting, KP Murphy, JJ Little CVPR 2011, 3249-3256, 2011 | 96 | 2011 |
| A Bayesian Approach to Nonlinear Parameter Identification for Rigid Body Dynamics. JA Ting, MN Mistry, J Peters, S Schaal, J Nakanishi Robotics: Science and systems, 32-39, 2006 | 87 | 2006 |
| Learning an outlier-robust Kalman filter JA Ting, E Theodorou, S Schaal Machine Learning: ECML 2007: 18th European Conference on Machine Learning …, 2007 | 85 | 2007 |
| Automatic outlier detection: A Bayesian approach JA Ting, A D'Souza, S Schaal Proceedings 2007 IEEE International Conference on Robotics and Automation …, 2007 | 72 | 2007 |
| Learning attentional policies for tracking and recognition in video with deep networks L Bazzani, H Larochelle, V Murino, J Ting, ND Freitas Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011 | 58 | 2011 |
| Variational Bayesian least squares: An application to brain–machine interface data JA Ting, A D’Souza, K Yamamoto, T Yoshioka, D Hoffman, S Kakei, ... Neural Networks 21 (8), 1112-1131, 2008 | 55 | 2008 |
| Active estimation of object dynamics parameters with tactile sensors HP Saal, JA Ting, S Vijayakumar 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2010 | 49 | 2010 |
| Little, and Kevin P Murphy. Learning to track and identify players from broadcast sports videos WL Lu, JA Ting, J James IEEE transactions on pattern analysis and machine intelligence 35 (7), 1704-1716, 2013 | 42 | 2013 |
| Bayesian robot system identification with input and output noise JA Ting, A D’Souza, S Schaal Neural Networks 24 (1), 99-108, 2011 | 32 | 2011 |
| Efficient learning and feature selection in high-dimensional regression JA Ting, A D'Souza, S Vijayakumar, S Schaal Neural computation 22 (4), 831-886, 2010 | 32 | 2010 |
| Active sequential learning with tactile feedback H Saal, JA Ting, S Vijayakumar Proceedings of the Thirteenth International Conference on Artificial …, 2010 | 27 | 2010 |
| Locally Weighted Regression for Control. JA Ting, S Vijayakumar, S Schaal Encyclopedia of Machine Learning 11, 613-624, 2010 | 25 | 2010 |
| Bayesian kernel shaping for learning control JA Ting, M Kalakrishnan, S Vijayakumar, S Schaal Advances in neural information processing systems 21, 2008 | 23 | 2008 |
| A bayesian approach to empirical local linearization for robotics JA Ting, A D'Souza, S Vijayakumar, S Schaal 2008 IEEE International Conference on Robotics and Automation, 2860-2865, 2008 | 21 | 2008 |
| Predicting EMG data from M1 neurons with variational Bayesian least squares JA Ting, A D'souza, K Yamamoto, T Yoshioka, D Hoffman, S Kakei, ... Advances in neural information processing systems 18, 2005 | 20 | 2005 |
| Bayesian regression with input noise for high dimensional data JA Ting, A D'souza, S Schaal Proceedings of the 23rd International Conference on Machine Learning, 937-944, 2006 | 16 | 2006 |
| Learning attentional mechanisms for simultaneous object tracking and recognition with deep networks L Bazzani, N de Freitas, JA Ting NIPS 2010 Deep Learning and Unsupervised Feature Learning Workshop 32, 3, 2010 | 12 | 2010 |
| Method for personalized context-aware, and privacy preserving real-time brokerage for advertising S Srinivasan, J Heit, C Soares, JA Ting US Patent App. 13/828,817, 2014 | 6 | 2014 |
Stefan SchaalRobotics, Machine Learning, AI, NeuroscienceVerified email at google.com
