ArticleProceedings of the VLDB Endowment. International Conference on Very Large Data Bases2018
Snuba: Automating Weak Supervision to Label Training Data.
Article in Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
5 citing papers in PubMed.
- Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach.Genome research · 2025Article
- Rule-Enhanced Active Learning for Semi-Automated Weak Supervision.Artificial intelligence · 2022Article
- Large-scale Vietnamese point-of-interest classification using weak labeling.Frontiers in artificial intelligence · 2022Article
- Amplifying Domain Expertise in Clinical Data Pipelines.JMIR medical informatics · 2020Article
- Snorkel: rapid training data creation with weak supervision.The VLDB journal : very large data bases : a publication of the VLDB Endowment · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
As deep learning models are applied to increasingly diverse problems, a key bottleneck is gathering enough high-quality training labels tailored to each task. Users therefore turn to
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.