| Factored particles for scalable monitoring B Ng, L Peshkin, A Pfeffer arXiv preprint arXiv:1301.0590, 2012 | 88 | 2012 |
| Continuous time particle filtering B Ng, A Pfeffer, R Dearden International joint conference on artificial intelligence 19, 1360, 2005 | 61 | 2005 |
| Towards applying interactive POMDPs to real-world adversary modeling B Ng, C Meyers, K Boakye, J Nitao Proceedings of the AAAI Conference on Artificial Intelligence 24 (2), 1814-1820, 2010 | 51 | 2010 |
| A flexible uncertainty quantification method for linearly coupled multi-physics systems X Chen, B Ng, Y Sun, C Tong Journal of Computational Physics 248, 383-401, 2013 | 34 | 2013 |
| Factored particle filtering for data fusion and situation assessment in urban environments S Das, D Lawless, B Ng, A Pfeffer 2005 7th International Conference on Information Fusion 2, 8 pp., 2005 | 31 | 2005 |
| A framework for optimization and quantification of uncertainty and sensitivity for developing carbon capture systems JC Eslick, B Ng, Q Gao, CH Tong, NV Sahinidis, DC Miller Energy Procedia 63, 1055-1063, 2014 | 30 | 2014 |
| Bayes-adaptive interactive POMDPs B Ng, K Boakye, C Meyers, A Wang Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 1408-1414, 2012 | 26 | 2012 |
| Factored reasoning for monitoring dynamic team and goal formation A Pfeffer, S Das, D Lawless, B Ng Information Fusion 10 (1), 99-106, 2009 | 24 | 2009 |
| Innovative computational tools and models for the design, optimization and control of carbon capture processes DC Miller, D Agarwal, D Bhattacharyya, J Boverhof, Y Chen, J Eslick, ... Process Systems and Materials for CO2 Capture: Modelling, Design, Control …, 2017 | 23 | 2017 |
| Advanced computational tools for optimization and uncertainty quantification of carbon capture processes DC Miller, B Ng, J Eslick, C Tong, Y Chen Computer Aided Chemical Engineering 34, 202-211, 2014 | 23 | 2014 |
| Attend and decode: 4d fmri task state decoding using attention models S Nguyen, B Ng, AD Kaplan, P Ray Machine Learning for Health, 267-279, 2020 | 22 | 2020 |
| Sequential design of experiments to maximize learning from carbon capture pilot plant testing FB Soepyan, CM Anderson-Cook, JC Morgan, CH Tong, D Bhattacharyya, ... Computer aided chemical engineering 44, 283-288, 2018 | 20 | 2018 |
| Active learning with deep Bayesian neural network for laser control TC Galvin, SI Herriot, B Ng, WH Williams, SS Talathi, T Spinka, ... Optics and Photonics for Information Processing XII 10751, 133-143, 2018 | 13 | 2018 |
| Integrated dynamic modeling and advanced process control of carbon capture systems P Mahapatra, J Ma, B Ng, D Bhattacharyya, SE Zitney, DC Miller Energy Procedia 63, 1354-1367, 2014 | 11 | 2014 |
| Adaptive dynamic Bayesian networks BM Ng Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States), 2007 | 11 | 2007 |
| Survey of anomaly detection methods B Ng Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States), 2006 | 11 | 2006 |
| Toward a multimodal-deep learning retrieval system for monitoring nuclear Proliferation Activities Y Feldman, M Arno, C Carrano, B Ng, B Chen Journal of Nuclear Materials Management 46 (3), 68-80, 2018 | 9 | 2018 |
| A computational method for simulating subsurface flow and reactive transport in heterogeneous porous media embedded with flexible uncertainty quantification X Chen, BM Ng, Y Sun, CH Tong Water Resources Research 49 (9), 5740-5755, 2013 | 9 | 2013 |
| Incremental thin junction trees for dynamic Bayesian networks F Hutter, B Ng, R Dearden Darmstadt University of Technology, Tech. Rep, 2004 | 7 | 2004 |
| Factored inference for efficient reasoning of complex dynamic systems BM Ng Harvard University, 2006 | 6 | 2006 |
