Generating Design Suggestions under Tight Constraints with Gradient-based Probabilistic Programming

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dc.contributor.authorRitchie, Danielen_US
dc.contributor.authorLin, Sharonen_US
dc.contributor.authorGoodman, Noah D.en_US
dc.contributor.authorHanrahan, Paten_US
dc.contributor.editorOlga Sorkine-Hornung and Michael Wimmeren_US
dc.date.accessioned2015-04-16T07:45:48Z
dc.date.available2015-04-16T07:45:48Z
dc.date.issued2015en_US
dc.description.abstractWe 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.en_US
dc.description.number2en_US
dc.description.sectionheadersShape Collectionsen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume34en_US
dc.identifier.doi10.1111/cgf.12580en_US
dc.identifier.pages515-526en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12580en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleGenerating Design Suggestions under Tight Constraints with Gradient-based Probabilistic Programmingen_US
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