ArticleProceedings of the VLDB Endowment. International Conference on Very Large Data Bases2017
Snorkel: Rapid Training Data Creation with Weak Supervision.
Article in Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
71 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Declarative Learning-Based Programming as an Interface to AI Systems.Frontiers in artificial intelligence · 2022Pooled it
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- PhyGeo-KG: Physics-Regularized Distant Supervision for Multimodal Geometric Knowledge Graph Construction in Catenary Maintenance.Sensors (Basel, Switzerland) · 2026Article
- AI-driven quality enhancement of medical datasets: a closed-loop framework of synthesis, annotation, quality control, and integration.Frontiers in medicine · 2026Review
- Multimodal text-audio sentiment in clinical aphasia speech using NLP.Frontiers in digital health · 2026Article
- Automated Shoulder Girdle Rigidity Assessment in Parkinson's Disease via an Integrated Model- and Data-Driven Approach.Sensors (Basel, Switzerland) · 2025Article
- Review
- A probabilistic approach for building disease phenotypes across electronic health records.BioData mining · 2025Article
- Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach.JMIR formative research · 2025Article
- Clinical entity augmented retrieval for clinical information extraction.NPJ digital medicine · 2025Article
- Weakly supervised veracity classification with LLM-predicted credibility signals.EPJ data science · 2025Article
- Weakly-Supervised Transfer Learning with Application in Precision Medicine.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2024Article
- Natural Language Processing-Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social Media: System Development and Validation.JMIR medical informatics · 2024Article
- Artificial intelligence-based video monitoring of movement disorders in the elderly: a review on current and future landscapes.Singapore medical journal · 2024Review
- Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models.BMC medical informatics and decision making · 2024Article
- Challenges in multi-task learning for fMRI-based diagnosis: Benefits for psychiatric conditions and CNVs would likely require thousands of patients.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Selective prediction for extracting unstructured clinical data.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- A weak-labelling and deep learning approach for in-focus object segmentation in 3D widefield microscopy.Scientific reports · 2023Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
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 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, Snorkel provides 132% average improvements to predictive performance over prior heuristic approaches and comes within an average 3.60% of the predictive performance of large hand-curated training sets.
Identifiers
What OpenQuestion holds
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.