490 citations · 1k across the 74 of their papers we have counts for
5 papers · 1 filter
Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs
Yutian Chen, Vikash Mansinghka, Zoubin Ghahramani
Probabilistic programming languages can simplify the development of machine learning techniques, but only if inference is sufficiently scalable. Unfortunately, Bayesian parameter e…
Inverse Graphics with Probabilistic CAD Models
Tejas D. Kulkarni, Vikash K. Mansinghka, Pushmeet Kohli +1
Recently, multiple formulations of vision problems as probabilistic inversions of generative models based on computer graphics have been proposed. However, applications to 3D perce…
Venture: a higher-order probabilistic programming platform with programmable inference
Vikash Mansinghka, Daniel Selsam, Yura Perov
We describe Venture, an interactive virtual machine for probabilistic programming that aims to be sufficiently expressive, extensible, and efficient for general-purpose use. Like C…
Building fast Bayesian computing machines out of intentionally stochastic, digital parts
Vikash Mansinghka, Eric Jonas
The brain interprets ambiguous sensory information faster and more reliably than modern computers, using neurons that are slower and less reliable than logic gates. But Bayesian in…
Variational Particle Approximations
Ardavan Saeedi, Tejas D Kulkarni, Vikash Mansinghka +1
Approximate inference in high-dimensional, discrete probabilistic models is a central problem in computational statistics and machine learning. This paper describes discrete partic…