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Stephen H. Bach

Researcher at Brown University

Publications -  49
Citations -  3823

Stephen H. Bach is an academic researcher from Brown University. The author has contributed to research in topics: Inference & Computer science. The author has an hindex of 19, co-authored 38 publications receiving 2392 citations. Previous affiliations of Stephen H. Bach include Stanford University & Georgetown University.

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Snorkel: rapid training data creation with weak supervision

Alexander Ratner, +5 more
TL;DR: Snorkel as mentioned in this paper is a system that enables users to train state-of-the-art models without hand labeling any training data, which can have unknown accuracies and correlations.
Proceedings ArticleDOI

Interpretable Decision Sets: A Joint Framework for Description and Prediction

Himabindu Lakkaraju, +2 more
TL;DR: This work proposes interpretable decision sets, a framework for building predictive models that are highly accurate, yet also highly interpretable, and provides a new approach to interpretable machine learning that balances accuracy, interpretability, and computational efficiency.
Journal ArticleDOI

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Teven Le Scao, +386 more
- 09 Nov 2022 - 
TL;DR: BLOOM as discussed by the authors is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total).
Posted Content

Hinge-Loss Markov Random Fields and Probabilistic Soft Logic

Stephen H. Bach, +3 more
- 17 May 2015 - 
TL;DR: In this paper, hinge-loss Markov random fields (HL-MRFs) and probabilistic soft logic (PSL) are proposed to model rich, structured data at scales not previously possible.
Proceedings Article

A short introduction to probabilistic soft logic

Angelika Kimmig, +4 more
TL;DR: This paper provides an overview of the PSL language and its techniques for inference and weight learning.