Mansinghka, Vikash K.

Church: a language for generative models

Goodman, N., Mansinghka, V., Roy, D. M., Bonawitz, K., & Tenenbaum, J. B. (2012) · arXiv

Bayesian Modeling and Causal InferenceLogic, programming, and type systems

Cited by 577

Reconciling intuitive physics and Newtonian mechanics for colliding objects

Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2013) · Psychological Review

Visual perception and processing mechanismsCategorization, perception, and language

Cited by 189

From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought

Wong, L., Grand, G., Lew, A. K., Goodman, N. D., Mansinghka, V. K., Andreas, J., & Tenenbaum, J. B. (2023) · arXiv

Logic, Reasoning, and KnowledgeNatural Language Processing Techniques

Cited by 36

3DP3: 3D Scene Perception via Probabilistic Programming

Gothoskar, N., Cusumano-Towner, M., Zinberg, B., Ghavamizadeh, M., Pollok, F., Garrett, A., Tenenbaum, J. B., Gutfreund, D., & Mansinghka, V. K. (2021) · arXiv

3D Shape Modeling and AnalysisRobot Manipulation and Learning

Cited by 18

Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes

Gothoskar, N., Ghavami, M., Li, E., Curtis, A., Noseworthy, M., Chung, K., Patton, B., Freeman, W. T., Tenenbaum, J. B., Klukas, M., et al. (2023) · arXiv

3D Shape Modeling and AnalysisRobot Manipulation and Learning

Cited by 5

Sequential Monte Carlo Learning for Time Series Structure Discovery

Saad, F. A., Patton, B. J., Hoffman, M. D., Saurous, R. A., & Mansinghka, V. K. (2023) · arXiv

Gaussian Processes and Bayesian InferenceMarkov Chains and Monte Carlo Methods

Cited by 3