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Snorkel: Fast Training Set Generation for Information Extraction.

, , , and . SIGMOD Conference, page 1683-1686. ACM, (2017)

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Hinge-Loss Markov Random Fields and Probabilistic Soft Logic., , , and . J. Mach. Learn. Res., (2017)Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs., , , and . ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 381-390. JMLR.org, (2015)Learning to Compose Soft Prompts for Compositional Zero-Shot Learning., , and . CoRR, (2022)Weakly Supervised Sequence Tagging from Noisy Rules., , and . AAAI, page 5570-5578. AAAI Press, (2020)Does CLIP Bind Concepts? Probing Compositionality in Large Image Models., , , , , , and . EACL (Findings), page 1487-1500. Association for Computational Linguistics, (2024)Snorkel: rapid training data creation with weak supervision., , , , , and . VLDB J., 29 (2-3): 709-730 (2020)Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations., , , , and . AIES, page 203-214. ACM, (2022)Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale., , , , , , , , , and 3 other author(s). SIGMOD Conference, page 362-375. ACM, (2019)Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning., , and . CoRR, (2023)Pseudo Shots: Few-Shot Learning with Auxiliary Data., , and . CoRR, (2020)