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Vikash K. Mansinghka

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

Dec 14, 2023
Nishad Gothoskar, Matin Ghavami, Eric Li, Aidan Curtis, Michael Noseworthy, Karen Chung, Brian Patton, William T. Freeman, Joshua B. Tenenbaum, Mirko Klukas, Vikash K. Mansinghka

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Sequential Monte Carlo Learning for Time Series Structure Discovery

Jul 13, 2023
Feras A. Saad, Brian J. Patton, Matthew D. Hoffman, Rif A. Saurous, Vikash K. Mansinghka

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Differentiating Metropolis-Hastings to Optimize Intractable Densities

Jun 30, 2023
Gaurav Arya, Ruben Seyer, Frank Schäfer, Kartik Chandra, Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka, Jonathan Ragan-Kelley, Christopher Rackauckas, Moritz Schauer

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

Jun 23, 2023
Lionel Wong, Gabriel Grand, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, Joshua B. Tenenbaum

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Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs

Jun 05, 2023
Alexander K. Lew, Tan Zhi-Xuan, Gabriel Grand, Vikash K. Mansinghka

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$ω$PAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs

Feb 21, 2023
Mathieu Huot, Alexander K. Lew, Vikash K. Mansinghka, Sam Staton

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3D Neural Embedding Likelihood for Robust Sim-to-Real Transfer in Inverse Graphics

Feb 07, 2023
Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka

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ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images

Oct 27, 2022
Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka, Rif A. Saurous

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