786 citations · 2k across the 26 of their papers we have counts for
4 papers · 1 filter
Snorkel: Rapid Training Data Creation with Weak Supervision
Alexander Ratner, Stephen H. Bach, Henry Ehrenberg +3
Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train stat…
Learning to Compose Domain-Specific Transformations for Data Augmentation
Alexander J. Ratner, Henry R. Ehrenberg, Zeshan Hussain +2
Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels. While it i…
SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data
Jason Fries, Sen Wu, Alex Ratner +1
We present SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly and without hand-labeled data. Our approach views biomedical resources lik…
Learning the Structure of Generative Models without Labeled Data
Stephen H. Bach, Bryan He, Alexander Ratner +1
Curating labeled training data has become the primary bottleneck in machine learning. Recent frameworks address this bottleneck with generative models to synthesize labels at scale…