41 citations · 45 across the 4 of their papers we have counts for
4 papers
Alfred: A System for Prompted Weak Supervision
Peilin Yu, Stephen H. Bach
Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting. In contrast to typical PWS systems where weak super…
Learning to Compose Soft Prompts for Compositional Zero-Shot Learning
Nihal V. Nayak, Peilin Yu, Stephen H. Bach
We introduce compositional soft prompting (CSP), a parameter-efficient learning technique to improve the zero-shot compositionality of large-scale pretrained vision-language models…
Learning from Multiple Noisy Partial Labelers
Peilin Yu, Tiffany Ding, Stephen H. Bach
Programmatic weak supervision creates models without hand-labeled training data by combining the outputs of heuristic labelers. Existing frameworks make the restrictive assumption…
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction
Shun Zheng, Xu Han, Yankai Lin +5
Pattern-based labeling methods have achieved promising results in alleviating the inevitable labeling noises of distantly supervised neural relation extraction. However, these meth…