79 citations · 338 across the 27 of their papers we have counts for
3 papers · 1 filter
Learning to Infer Graphics Programs from Hand-Drawn Images
Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama +1
We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of \LaTeX. The model combines techniques from deep learning and program…
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…
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching
Daniel Ritchie, Andreas Stuhlmüller, Noah D. Goodman
Lightweight, source-to-source transformation approaches to implementing MCMC for probabilistic programming languages are popular for their simplicity, support of existing determini…