Snorkel: Rapid Training Data Creation with Weak Supervision

Proceedings of the VLDB Endowment
Alexander J RatnerChristopher Ré

Abstract

Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of- the-art models without hand labeling any training data. Instead, users write labeling functions that express arbitrary heuristics, which can have unknown accuracies and correlations. Snorkel denoises their outputs without access to ground truth by incorporating the first end-to-end implementation of our recently proposed machine learning paradigm, data programming. We present a flexible interface layer for writing labeling functions based on our experience over the past year collaborating with companies, agencies, and research labs. In a user study, subject matter experts build models 2.8× faster and increase predictive performance an average 45.5% versus seven hours of hand labeling. We study the modeling tradeoffs in this new setting and propose an optimizer for automating tradeoff decisions that gives up to 1.8× speedup per pipeline execution. In two collaborations, with the U.S. Department of Veterans Affairs and the U.S. Food and Drug Administration, and on four open-source text and image data sets representative of other deployments, Snork...Continue Reading

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Citations

Oct 5, 2019·NPJ Digital Medicine·Alison CallahanNigam H Shah
Nov 30, 2019·Proceedings of the VLDB Endowment·Paroma Varma, Christopher Ré
Jan 22, 2020·Scientific Reports·Pratik DuttaAviral Kumar
May 29, 2020·BMC Bioinformatics·Emily K MalloryRuss B Altman
May 1, 2020·NPJ Digital Medicine·Khaled SaabChristopher Lee-Messer
Dec 6, 2019·BMC Medical Informatics and Decision Making·Xuedong LiQiaozhu Mei
Jul 9, 2020·Computational and Structural Biotechnology Journal·David N Nicholson, Casey S Greene
Jan 9, 2021·Proceedings of the National Academy of Sciences of the United States of America·Steven T SmithDonald B Rubin
Jul 22, 2020·International Journal of Medical Informatics·Lishan Yu, Sheng Yu
Feb 19, 2021·NPJ Digital Medicine·Khaled SaabChristopher Lee-Messer
Jun 4, 2021·Scientific Data·Lisa Grossman LiuDavid K Vawdrey
May 29, 2021·Journal of Medical Internet Research·Emad Kasaeyan NaeiniAmir M Rahmani
Nov 10, 2021·Journal of Chemical Information and Modeling·Pranav Shetty, Rampi Ramprasad
Nov 11, 2021··Fabio MarescaFlavio Cirillo
Jun 18, 2021··Matthias Boehm, Svetlana Sagadeeva
May 4, 2020··Anna Tigunova, Anna Tigunova
Jul 26, 2019··Xiao YanMikhail Obukhov
May 27, 2020··Tongshuang WuChris DuBois
Jul 26, 2019··Xin Luna DongTheodoros Rekatsinas
Nov 4, 2019··Amit SomechTova Milo
May 25, 2018··Theodoros RekatsinasPhilip Levis
Jul 19, 2018··Ahmed ElmagarmidAbdulhakim A. Qahtan
May 25, 2018··Xin Luna DongTheodoros Rekatsinas
May 25, 2018··Ganesh KrishnanAnHai Doan
Jul 21, 2020··Reza RejaieWalter Willinger
Sep 21, 2020··Marnith PengRavigopal Vennelakanti
Nov 11, 2019··Siqi LiuJanhavi Mahajan
Aug 20, 2020··Clara H. McCreeryAnitha Kannan
Aug 20, 2020··Udi WeinsbergDimitris Kalimeris
Jul 19, 2019··Joydeep GhoshDany Haddad

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