786 citations · 1.9k across the 20 of their papers we have counts for
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Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning
Cristina Menghini, Andrew Delworth, Stephen H. Bach
Fine-tuning vision-language models (VLMs) like CLIP to downstream tasks is often necessary to optimize their performance. However, a major obstacle is the limited availability of l…
An Adaptive Method for Weak Supervision with Drifting Data
Alessio Mazzetto, Reza Esfandiarpoor, Akash Singirikonda +2
We introduce an adaptive method with formal quality guarantees for weak supervision in a non-stationary setting. Our goal is to infer the unknown labels of a sequence of data by us…
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…