17 citations · 59 across the 17 of their papers we have counts for
17 papers
GradInf: Gradient Estimation as Probabilistic Inference
Gaurav Arya, Mathieu Huot, Moritz Schauer +2
Gradient estimation -- the task of computing the gradient of the expected value of a probabilistic program -- has diverse applications in scientific computing, but is notoriously d…
GenMatter: Perceiving Physical Objects with Generative Matter Models
Eric Li, Arijit Dasgupta, Yoni Friedman +5
Human visual perception offers valuable insights for understanding computational principles of motion-based scene interpretation. Humans robustly detect and segment moving entities…
Probabilistic Programming with Programmable Variational Inference
McCoy R. Becker, Alexander K. Lew, Xiaoyan Wang +4
Compared to the wide array of advanced Monte Carlo methods supported by modern probabilistic programming languages (PPLs), PPL support for variational inference (VI) is less develo…
GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables
Mathieu Huot, Matin Ghavami, Alexander K. Lew +6
This article presents GenSQL, a probabilistic programming system for querying probabilistic generative models of database tables. By augmenting SQL with only a few key primitives f…
Differentiating Metropolis-Hastings to Optimize Intractable Densities
Gaurav Arya, Ruben Seyer, Frank Schäfer +7
We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers, allowing us to differentiate through probabilistic inference, even if the model has discrete…
SD: Differentiable Programming for Sparse Tensors
Amir Shaikhha, Mathieu Huot, Shideh Hashemian
Sparse tensors are prevalent in many data-intensive applications, yet existing differentiable programming frameworks are tailored towards dense tensors. This presents a significant…