79 citations · 338 across the 27 of their papers we have counts for
Showing 2016Show all
2 papers · 1 filter
cs.AI2016★ 38 cited
Deep Amortized Inference for Probabilistic Programs
Daniel Ritchie, Paul Horsfall, Noah D. Goodman
Probabilistic programming languages (PPLs) are a powerful modeling tool, able to represent any computable probability distribution. Unfortunately, probabilistic program inference i…
cs.GR2016★ 32 cited
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks
Daniel Ritchie, Anna Thomas, Pat Hanrahan +1
Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) provide powerful tools for constraining procedural models in computer graphics, but they require many sample…