Vikash K. Mansinghka
Vikash K. Mansinghka
MIT, Probabilistic Computing Project
Verified email at - Homepage
Cited by
Cited by
Church: a language for generative models
N Goodman, V Mansinghka, DM Roy, K Bonawitz, JB Tenenbaum
arXiv preprint arXiv:1206.3255, 2012
A new approach to probabilistic programming inference
F Wood, JW Meent, V Mansinghka
Artificial Intelligence and Statistics, 1024-1032, 2014
Venture: a higher-order probabilistic programming platform with programmable inference
V Mansinghka, D Selsam, Y Perov
arXiv preprint arXiv:1404.0099, 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
Reconciling intuitive physics and Newtonian mechanics for colliding objects.
AN Sanborn, VK Mansinghka, TL Griffiths
Psychological review 120 (2), 411, 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
Approximate bayesian image interpretation using generative probabilistic graphics programs
VK Mansinghka, TD Kulkarni, YN Perov, JB Tenenbaum
arXiv preprint arXiv:1307.0060, 2013
Structured priors for structure learning
V Mansinghka, C Kemp, T Griffiths, J Tenenbaum
arXiv preprint arXiv:1206.6852, 2012
Learning annotated hierarchies from relational data
DM Roy, C Kemp, V Mansinghka, J B Tenenbaum
Carnegie Mellon University, 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
Natively probabilistic computation
VK Mansinghka
Massachusetts Institute of Technology, Department of Brain and Cognitive …, 2009
Natively probabilistic computation
VK Mansinghka
Massachusetts Institute of Technology, Department of Brain and Cognitive …, 2009
A probabilistic model of cross-categorization
P Shafto, C Kemp, V Mansinghka, JB Tenenbaum
Cognition 120 (1), 1-25, 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
Combinational stochastic logic
VK Mansinghka, EM Jonas
US Patent 8,352,384, 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
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
Variational particle approximations
A Saeedi, TD Kulkarni, VK Mansinghka, SJ Gershman
The Journal of Machine Learning Research 18 (1), 2328-2356, 2017
Stochastic digital circuits for probabilistic inference
VK Mansinghka, EM Jonas, JB Tenenbaum
Massachussets Institute of Technology, Technical Report MITCSAIL-TR 2069, 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
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