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Vikash K. Mansinghka
Vikash K. Mansinghka
MIT, Probabilistic Computing Project
Подтвержден адрес электронной почты в домене mit.edu - Главная страница
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Процитировано
Год
Church: a language for generative models
N Goodman, V Mansinghka, DM Roy, K Bonawitz, JB Tenenbaum
arXiv preprint arXiv:1206.3255, 2012
9962012
A new approach to probabilistic programming inference
F Wood, JW Meent, V Mansinghka
Artificial intelligence and statistics, 1024-1032, 2014
4052014
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
3332012
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
2502015
Reconciling intuitive physics and Newtonian mechanics for colliding objects.
AN Sanborn, VK Mansinghka, TL Griffiths
Psychological review 120 (2), 411, 2013
2462013
Venture: a higher-order probabilistic programming platform with programmable inference
V Mansinghka, D Selsam, Y Perov
arXiv preprint arXiv:1404.0099, 2014
2372014
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
2332019
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
1412013
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
1342006
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
1162020
Structured priors for structure learning
V Mansinghka, C Kemp, T Griffiths, J Tenenbaum
arXiv preprint arXiv:1206.6852, 2012
1032012
From word models to world models: Translating from natural language to the probabilistic language of thought
L Wong, G Grand, AK Lew, ND Goodman, VK Mansinghka, J Andreas, ...
arXiv preprint arXiv:2306.12672, 2023
932023
Learning annotated hierarchies from relational data
DM Roy, C Kemp, V Mansinghka, J Tenenbaum
Advances in neural information processing systems 19, 2006
792006
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
732019
Natively probabilistic computation
VK Mansinghka
Massachusetts Institute of Technology, Department of Brain and Cognitive …, 2009
722009
From machine learning to robotics: Challenges and opportunities for embodied intelligence
N Roy, I Posner, T Barfoot, P Beaudoin, Y Bengio, J Bohg, O Brock, ...
arXiv preprint arXiv:2110.15245, 2021
712021
A probabilistic model of cross-categorization
P Shafto, C Kemp, V Mansinghka, JB Tenenbaum
Cognition 120 (1), 1-25, 2011
672011
Brain-wide representations of behavior spanning multiple timescales and states in C. elegans
AA Atanas, J Kim, Z Wang, E Bueno, MC Becker, D Kang, J Park, ...
Cell 186 (19), 4134-4151. e31, 2023
602023
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
602018
3DP3: 3D scene perception via probabilistic programming
N Gothoskar, M Cusumano-Towner, B Zinberg, M Ghavamizadeh, ...
Advances in Neural Information Processing Systems 34, 9600-9612, 2021
572021
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