Generating Design Suggestions under Tight Constraints with Gradient-based Probabilistic Programming
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Date
2015
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
We present a system for generating suggestions from highly-constrained, continuous design spaces. We formulate suggestion as sampling from a probability distribution; constraints are represented as factors that concentrate probability mass around sub-manifolds of the design space. These sampling problems are intractable using typical random walk MCMC techniques, so we adopt Hamiltonian Monte Carlo (HMC), a gradient-based MCMC method. We implement HMC in a high-performance probabilistic programming language, and we evaluate its ability to efficiently generate suggestions for two different, highly-constrained example applications: vector art coloring and designing stable stacking structures.
Description
@article{10.1111:cgf.12580,
journal = {Computer Graphics Forum},
title = {{Generating Design Suggestions under Tight Constraints with Gradient-based Probabilistic Programming}},
author = {Ritchie, Daniel and Lin, Sharon and Goodman, Noah D. and Hanrahan, Pat},
year = {2015},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
DOI = {10.1111/cgf.12580}
}