ArticleProceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence
Training Complex Models with Multi-Task Weak Supervision.
Article in Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Mathematical and Algorithmic Advances in Machine Learning for Statistical Process Control: A Systematic Review.Entropy (Basel, Switzerland) · 2026Review
- A probabilistic approach for building disease phenotypes across electronic health records.BioData mining · 2025Article
- Article
- Scoping review and classification of deep learning in medical genetics.Genetics in medicine : official journal of the American College of Medical Genetics · 2022Article
- Rule-Enhanced Active Learning for Semi-Automated Weak Supervision.Artificial intelligence · 2022Article
- Application Research for Fusion Model of Pseudolabel and Cross Network.Computational intelligence and neuroscience · 2022Article
- ACE: the Advanced Cohort Engine for searching longitudinal patient records.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Ontology-driven weak supervision for clinical entity classification in electronic health records.Nature communications · 2021Article
- Extracting chemical reactions from text using Snorkel.BMC bioinformatics · 2020Article
- Cross-Modal Data Programming Enables Rapid Medical Machine Learning.Patterns (New York, N.Y.) · 2020Article
- Snorkel: rapid training data creation with weak supervision.The VLDB journal : very large data bases : a publication of the VLDB Endowment · 2020Article
- Medical device surveillance with electronic health records.NPJ digital medicine · 2019Article
- Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale.Proceedings. ACM-SIGMOD International Conference on Management of DataArticle
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
As machine learning models continue to increase in complexity, collecting large hand-labeled training sets has become one of the biggest roadblocks in practice. Instead, weaker forms of supervision that provide noisier but cheaper labels are often used. However, these weak supervision sources have diverse and unknown accuracies, may output correlated labels, and may label different tasks or apply at different levels of granularity. We propose a framework for integrating and modeling such weak supervision sources by viewing them as labeling different related sub-tasks of a problem, which we refer to as the
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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.