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Snuba: Automating Weak Supervision to Label Training Data
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 weak supervision, relying on imperfect sources of labels like pattern matching and user-defined heuristics. Unfortuna...
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| Publicado no: | Proceedings VLDB Endowment |
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| Main Authors: | , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2018
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6879381/ https://ncbi.nlm.nih.gov/pubmed/31777681 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.14778/3291264.3291268 |
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