Snorkel: rapid training data creation with weak supervision

Snorkel: Rapid Training Data Creation with Weak Supervision

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 stateof-the-art models without hand labeling any training data. Instead, users write labeling functions that express arbitrary heuristics, which can have unknown accuracies and correlations. Snorkel denoises their outputs...

Visual Parsing with Weak Supervision

Snorkel: Beyond Hand-labeled Data

This talk describes Snorkel, a software system whose goal is to make routine machine learning tasks dramatically easier. Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets for a user’s task. In Snorkel, a user implicitly defines large training sets by writing simple programs that create labeled data, instead of tediously hand-...

Multi-Resolution Language Grounding with Weak Supervision

Language is given meaning through its correspondence with a world representation. This correspondence can be at multiple levels of granularity or resolutions. In this paper, we introduce an approach to multi-resolution language grounding in the extremely challenging domain of professional soccer commentaries. We define and optimize a factored objective function that allows us to leverage discou...

Learning Transformation Invariant Representations with Weak Supervision

Benjamin Coors1,2, Alexandru Condurache2, Alfred Mertins3 and Andreas Geiger1,4 1Autonomous Vision Group, MPI for Intelligent Systems, Tübingen, Germany 2Robert Bosch GmbH, Leonberg, Germany 3Institute for Signal Processing, University of Lübeck, Germany 4Computer Vision and Geometry Group, ETH Zürich, Switzerland {benjamin.coors,andreas.geiger}@tue.mpg.de, [email protected], m...