| Church: a language for generative models N Goodman, V Mansinghka, DM Roy, K Bonawitz, JB Tenenbaum arXiv preprint arXiv:1206.3255, 2012 | 822 | 2012 |
| A new approach to probabilistic programming inference F Wood, JW Meent, V Mansinghka Artificial Intelligence and Statistics, 1024-1032, 2014 | 306 | 2014 |
| Venture: a higher-order probabilistic programming platform with programmable inference V Mansinghka, D Selsam, Y Perov arXiv preprint arXiv:1404.0099, 2014 | 189 | 2014 |
| 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 | 182 | 2015 |
| Reconciling intuitive physics and Newtonian mechanics for colliding objects. AN Sanborn, VK Mansinghka, TL Griffiths Psychological review 120 (2), 411, 2013 | 148 | 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 | 105 | 2006 |
| Approximate bayesian image interpretation using generative probabilistic graphics programs VK Mansinghka, TD Kulkarni, YN Perov, JB Tenenbaum arXiv preprint arXiv:1307.0060, 2013 | 97 | 2013 |
| Structured priors for structure learning V Mansinghka, C Kemp, T Griffiths, J Tenenbaum arXiv preprint arXiv:1206.6852, 2012 | 85 | 2012 |
| Learning annotated hierarchies from relational data DM Roy, C Kemp, V Mansinghka, J B Tenenbaum Carnegie Mellon University, 2007 | 69 | 2007 |
| 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 | 63 | 2019 |
| Natively probabilistic computation VK Mansinghka Massachusetts Institute of Technology, Department of Brain and Cognitive …, 2009 | 59 | 2009 |
| Natively probabilistic computation VK Mansinghka Massachusetts Institute of Technology, Department of Brain and Cognitive …, 2009 | 59 | 2009 |
| A probabilistic model of cross-categorization P Shafto, C Kemp, V Mansinghka, JB Tenenbaum Cognition 120 (1), 1-25, 2011 | 55 | 2011 |
| 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 | 39 | 2006 |
| Combinational stochastic logic VK Mansinghka, EM Jonas US Patent 8,352,384, 2013 | 30 | 2013 |
| 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 | 27 | 2019 |
| Crosscat: A fully bayesian nonparametric method for analyzing heterogeneous, high dimensional data V Mansinghka, P Shafto, E Jonas, C Petschulat, M Gasner, ... MIT Press, 2016 | 27 | 2016 |
| Stochastic digital circuits for probabilistic inference VK Mansinghka, EM Jonas, JB Tenenbaum Massachussets Institute of Technology, Technical Report MITCSAIL-TR 2069, 2008 | 26 | 2008 |
| BayesDB: A probabilistic programming system for querying the probable implications of data V Mansinghka, R Tibbetts, J Baxter, P Shafto, B Eaves arXiv preprint arXiv:1512.05006, 2015 | 25 | 2015 |
| Particle Gibbs with ancestor sampling for probabilistic programs JW Meent, H Yang, V Mansinghka, F Wood Artificial Intelligence and Statistics, 986-994, 2015 | 24 | 2015 |
Joshua B. TenenbaumMITVerified email at mit.edu
