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This paper describes TAGLETS, a system built to study techniques for automatically exploiting all three types of data and creating high-quality, servable classifiers
We are honored to be part of the International Conference on Learning Representations (ICLR) 2022, where Snorkel AI founders and researchers will be presenting five papers on data-centric AI topics The field of artificial intelligence moves fast! This is a world we are intimately familiar with at Snorkel AI, having spun out of academia in 2019. For over half a…
Presenting Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules.
This paper proposes a universal technique that enables weak supervision over any label type while still offering desirable properties, including practical flexibility, computational efficiency, and theoretical guarantees.
This paper showcases how using a data-centric approach to generate high-quality training data at massive scale to improve the zero-shot abilities of that model.
This paper extends the scope of usable sources in WS, by formulating Weak Indirect Supervision (WIS), a new research problem for automatically synthesizing training labels based on indirect supervision sources that have different output label spaces.
The Future of Data-Centric AI Talk Series Background Chelsea Finn is an assistant professor of computer science and electrical engineering at Stanford University, whose research has been widely recognized, including in the New York Times and MIT Technology Review. In this talk, Chelsea talks about algorithms that use data from tasks you are interested in and data from other tasks….
This paper introduces the Structured State Space sequence model (s4), which uses a new parameterization for the state-space model to improve long-range dependency handling both mathematically and empirically.
This work enables users to create partial labelers that output subsets of possible class labels would greatly expand the expressivity of programmatic weak supervision.
