Labeling training data is a critical and expensive step in producing high accuracy ML
models, whether training from scratch or fine-tuning. To make labeling more efficient, two …

Foundation models offer an exciting new paradigm for constructing models with out-of-the-
box embeddings and a few labeled examples. However, it is not clear how to best apply …

Healthcare providers usually record detailed notes of the clinical care delivered to each
patient for clinical, research, and billing purposes. Due to the unstructured nature of these …

Our goal is to enable machine learning systems to be trained interactively. This requires
models that perform well and train quickly, without large amounts of hand-labeled data. We …

Abstract Powerful Machine Learning (ML) models trained on large, annotated datasets have
driven impressive advances in fields including natural language processing and computer …

Weak supervision is a broad set of techniques using weak sources of signal to supervise
models, such as distant supervision [Takamatsu et al., 2012], co-training methods [Blum and …