Software 2.0 and Snorkel: Beyond Hand-Labeled Data
This paper describes Snorkel, a system that enables users to help shape, create, and manage training data for Software 2.0 stacks.
Snorkel MeTaL: Weak Supervision for Multi-Task Learning
Presenting Snorkel MeTal, an end-to-end system for multi-task learning.
Fonduer: Knowledge Base Construction From Richly Formatted Data
Introducing Fonduer, a machine-learning-based KBC system for richly formatted data.
Deep Text Mining of Instagram Data Without Strong Supervision
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.
Snorkel: Fast Training Set Generation for Information Extraction
Introducing Snorkel, a new system for quickly creating, managing, and modeling training datasets.
Learning to Compose Domain-Specific Transformations for Data Augmentation
Automating data augmentation by learning a generative sequence model over user-specified transformation functions.
Learning the Structure of Generative Models Without Labeled Data
Proposing a structure estimation method that is 100x faster than a maximum likelihood approach for training data.
Inferring Generative Model Structure With Static Analysis
Presenting Coral, a paradigm that infers generative model structure, significantly reducing the amount of data required to learn structure.