65 citations · 97 across the 12 of their papers we have counts for
13 papers
Running Markov Chain Monte Carlo on Modern Hardware and Software
Pavel Sountsov, Colin Carroll, Matthew D. Hoffman
Today, cheap numerical hardware offers huge amounts of parallel computing power, much of which is used for the task of fitting neural networks to data. Adoption of this hardware to…
Robust Inverse Graphics via Probabilistic Inference
Tuan Anh Le, Pavel Sountsov, Matthew D. Hoffman +3
How do we infer a 3D scene from a single image in the presence of corruptions like rain, snow or fog? Straightforward domain randomization relies on knowing the family of corruptio…
Training Chain-of-Thought via Latent-Variable Inference
Du Phan, Matthew D. Hoffman, David Dohan +7
Large language models (LLMs) solve problems more accurately and interpretably when instructed to work out the answer step by step using a ``chain-of-thought'' (CoT) prompt. One can…
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images
Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov +4
The problem of inferring object shape from a single 2D image is underconstrained. Prior knowledge about what objects are plausible can help, but even given such prior knowledge the…
Adaptive Tuning for Metropolis Adjusted Langevin Trajectories
Lionel Riou-Durand, Pavel Sountsov, Jure Vogrinc +2
Hamiltonian Monte Carlo (HMC) is a widely used sampler for continuous probability distributions. In many cases, the underlying Hamiltonian dynamics exhibit a phenomenon of resonanc…
Nested : Assessing the convergence of Markov chain Monte Carlo when running many short chains
Charles C. Margossian, Matthew D. Hoffman, Pavel Sountsov +3
Recent developments in parallel Markov chain Monte Carlo (MCMC) algorithms allow us to run thousands of chains almost as quickly as a single chain, using hardware accelerators such…