Snorkel: Rapid Training Data Creation with Weak Supervision
Summary: Snorkel enables rapid ML training from weak supervision via labeling functions with unknown accuracies. End-to-end data programming denoises labels without ground truth, with a tradeoff optimizer, showing speedups and accuracy gains over hand labeling. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Alexander Ratner
- 2. Stephen H. Bach
- 3. Henry Ehrenberg
- 4. Jason Fries
- 5. Sen Wu
- 6. Christopher RĂ©
Incoming Citations (Sorted by Pagerank)
Showing 20 of 70 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 192 | HoloClean: Holistic Data Repairs with Probabilistic Inference | 2017 | VLDB | 0.00035692958 |
| 372 | A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration | 2012 | VLDB | 0.00025371138 |
| 394 | Big Data Integration | 2013 | VLDB | 0.0002447017 |
| 906 | Fusing Data with Correlations | 2014 | SIGMOD | 0.00015420344 |
| 3,900 | SLiMFast: Guaranteed Results for Data Fusion and Source Reliability | 2017 | SIGMOD | 6.649432e-05 |
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