| Church: a language for generative models N Goodman, V Mansinghka, DM Roy, K Bonawitz, JB Tenenbaum arXiv preprint arXiv:1206.3255, 2012 | 940 | 2012 |
| A new approach to probabilistic programming inference F Wood, JW Meent, V Mansinghka Artificial intelligence and statistics, 1024-1032, 2014 | 374 | 2014 |
| A short introduction to probabilistic soft logic A Kimmig, S Bach, M Broecheler, B Huang, L Getoor Proceedings of the NIPS workshop on probabilistic programming: foundations …, 2012 | 301 | 2012 |
| Picture: A probabilistic programming language for scene perception TD Kulkarni, P Kohli, JB Tenenbaum, V Mansinghka Proceedings of the ieee conference on computer vision and pattern …, 2015 | 235 | 2015 |
| Venture: a higher-order probabilistic programming platform with programmable inference V Mansinghka, D Selsam, Y Perov arXiv preprint arXiv:1404.0099, 2014 | 222 | 2014 |
| Reconciling intuitive physics and Newtonian mechanics for colliding objects. AN Sanborn, VK Mansinghka, TL Griffiths Psychological review 120 (2), 411, 2013 | 203 | 2013 |
| Gen: A general-purpose probabilistic programming system with programmable inference MF Cusumano-Towner, FA Saad, A Lew, VK and Mansinghka Technical Report MIT-CSAIL-TR-2018-020, Computer Science and Artificial …, 2019 | 176 | 2019 |
| Approximate bayesian image interpretation using generative probabilistic graphics programs VK Mansinghka, TD Kulkarni, YN Perov, J Tenenbaum Advances in Neural Information Processing Systems 26, 2013 | 126 | 2013 |
| Intuitive theories of mind: A rational approach to false belief ND Goodman, CL Baker, EB Bonawitz, VK Mansinghka, A Gopnik, ... Proceedings of the twenty-eighth annual conference of the cognitive science …, 2006 | 114 | 2006 |
| Structured priors for structure learning V Mansinghka, C Kemp, T Griffiths, J Tenenbaum arXiv preprint arXiv:1206.6852, 2012 | 100 | 2012 |
| Learning annotated hierarchies from relational data DM Roy, C Kemp, V Mansinghka, J Tenenbaum Advances in neural information processing systems 19, 2006 | 75 | 2006 |
| Natively probabilistic computation VK Mansinghka Massachusetts Institute of Technology, Department of Brain and Cognitive …, 2009 | 72 | 2009 |
| Natively probabilistic computation VK Mansinghka Massachusetts Institute of Technology, Department of Brain and Cognitive …, 2009 | 72 | 2009 |
| Online bayesian goal inference for boundedly rational planning agents T Zhi-Xuan, J Mann, T Silver, J Tenenbaum, V Mansinghka Advances in neural information processing systems 33, 19238-19250, 2020 | 67 | 2020 |
| A probabilistic model of cross-categorization P Shafto, C Kemp, V Mansinghka, JB Tenenbaum Cognition 120 (1), 1-25, 2011 | 64 | 2011 |
| Bayesian synthesis of probabilistic programs for automatic data modeling FA Saad, MF Cusumano-Towner, U Schaechtle, MC Rinard, ... Proceedings of the ACM on Programming Languages 3 (POPL), 1-32, 2019 | 59 | 2019 |
| Probabilistic programming with programmable inference VK Mansinghka, U Schaechtle, S Handa, A Radul, Y Chen, M and Rinard Proceedings of the 39th ACM SIGPLAN Conference on Programming Language …, 2018 | 48 | 2018 |
| Learning cross-cutting systems of categories P Shafto, C Kemp, V Mansinghka, M Gordon, JB Tenenbaum Proceedings of the 28th annual conference of the Cognitive Science Society …, 2006 | 43 | 2006 |
| Variational particle approximations A Saeedi, TD Kulkarni, VK Mansinghka, SJ Gershman The Journal of Machine Learning Research 18 (1), 2328-2356, 2017 | 40 | 2017 |
| Variational particle approximations A Saeedi, TD Kulkarni, VK Mansinghka, SJ Gershman The Journal of Machine Learning Research 18 (1), 2328-2356, 2017 | 38 | 2017 |
Joshua B. TenenbaumMITVerified email at mit.edu
