Resource library

Introducing BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision.

This paper describes Snorkel, a system that enables users to help shape, create, and manage training data for Software 2.0 stacks.

Presenting Snorkel MeTal, an end-to-end system for multi-task learning.

Introducing Fonduer, a machine-learning-based KBC system for richly formatted data.

This paper showcases methods for unsupervised mining of fashion attributes from Instagram text, which can enable a new kind of user recommendation in the fashion domain.

Introducing Snorkel, a new system for quickly creating, managing, and modeling training datasets.

Automating data augmentation by learning a generative sequence model over user-specified transformation functions.

Proposing a structure estimation method that is 100x faster than a maximum likelihood approach for training data.

Presenting Coral, a paradigm that infers generative model structure, significantly reducing the amount of data required to learn structure.