Flexible Approximate Inference via Stratified Normalizing Flows

Chris Cundy
Chris Cundy

UAI, pp. 1288-1297, 2020.

Cited by: 0|Bibtex|Views35
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Other Links: dblp.uni-trier.de|academic.microsoft.com

Abstract:

A major obstacle to forming posterior distributions in machine learning is the difficulty of evaluating partition functions. Monte-Carlo approaches are unbiased, but can suffer from high variance. Variational methods are biased, but tend to have lower variance. We develop an approximate inference procedure that allows explicit control of ...More

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