{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mcflow-monte-carlo-flow-models-for-data","title":"MCFlow: Monte Carlo Flow Models for Data Imputation","arxiv_id":"2003.12628","date":"2020-03-27","proceeding":"CVPR 2020 6","authors":["Trevor W. Richardson","Wencheng Wu","Lei Lin","Beilei Xu","Edgar A. Bernal"],"abstract":"We consider the topic of data imputation, a foundational task in machine learning that addresses issues with missing data. To that end, we propose MCFlow, a deep framework for imputation that leverages normalizing flow generative models and Monte Carlo sampling. 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