ArticleThe VLDB journal : very large data bases : a publication of the VLDB Endowment2020
Snorkel: rapid training data creation with weak supervision.
Article in The VLDB journal : very large data bases : a publication of the VLDB Endowment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- A weakly supervised transformer for rare disease diagnosis and subphenotyping from EHRs with pulmonary case studies.NPJ digital medicine · 2026Article
- Psychological decoupling in responses to AI: emotional reactivity and behavioral discourse in digital environments.Frontiers in psychiatry · 2026Article
- InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records.Nature communications · 2025Article
- A machine-learning-driven data labeling pipeline for scientific analysis inJournal of applied crystallography · 2025Article
- Self-supervised learning for label-free segmentation in cardiac ultrasound.Nature communications · 2025Article
- Predicting survival in prospective clinical trials using weakly-supervised QSP.NPJ precision oncology · 2025Article
- Emerging trends in SERS-based veterinary drug detection: multifunctional substrates and intelligent data approaches.NPJ science of food · 2025Review
- BidCorpus: A multifaceted learning dataset for public procurement.Data in brief · 2025Article
- Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach.Genome research · 2025Article
- Detecting suicide risk among U.S. servicemembers and veterans: a deep learning approach using social media data.Psychological medicine · 2024Article
- The changing landscape of text mining: a review of approaches for ecology and evolution.Proceedings. Biological sciences · 2024Review
- Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.Cell systems · 2024Article
- Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study.JMIR medical informatics · 2024Article
- Automatic categorization of self-acknowledged limitations in randomized controlled trial publications.Journal of biomedical informatics · 2024Article
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- Ontology-driven and weakly supervised rare disease identification from clinical notes.BMC medical informatics and decision making · 2023Article
- Improving classification of correct and incorrect protein-protein docking models by augmenting the training set.Bioinformatics advances · 2023Article
- Development and validation of MedDRA Tagger: a tool for extraction and structuring medical information from clinical notes.medRxiv : the preprint server for health sciences · 2022Article
- Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts.BioData mining · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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 laboratories. In a user study, subject matter experts build models
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.