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Inference Compilation and Universal Probabilistic Programming
[article]
2017
arXiv
pre-print
We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do "compilation of inference" because our method transforms a denotational specification of an inference problem in the form of a probabilistic program written in a universal programming language into a
arXiv:1610.09900v2
fatcat:e6cyizuc2fcypltpcr3x6pucoa