1 code implementation • 29 May 2023 • Peilin Yu, Stephen Bach
Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting.
2 code implementations • 30 Jun 2022 • Jason Alan Fries, Leon Weber, Natasha Seelam, Gabriel Altay, Debajyoti Datta, Samuele Garda, Myungsun Kang, Ruisi Su, Wojciech Kusa, Samuel Cahyawijaya, Fabio Barth, Simon Ott, Matthias Samwald, Stephen Bach, Stella Biderman, Mario Sänger, Bo wang, Alison Callahan, Daniel León Periñán, Théo Gigant, Patrick Haller, Jenny Chim, Jose David Posada, John Michael Giorgi, Karthik Rangasai Sivaraman, Marc Pàmies, Marianna Nezhurina, Robert Martin, Michael Cullan, Moritz Freidank, Nathan Dahlberg, Shubhanshu Mishra, Shamik Bose, Nicholas Michio Broad, Yanis Labrak, Shlok S Deshmukh, Sid Kiblawi, Ayush Singh, Minh Chien Vu, Trishala Neeraj, Jonas Golde, Albert Villanova del Moral, Benjamin Beilharz
Training and evaluating language models increasingly requires the construction of meta-datasets --diverse collections of curated data with clear provenance.
no code implementations • 28 Sep 2020 • Nihal Nayak, Stephen Bach
We propose to learn class representations from common sense knowledge graphs.
no code implementations • 26 Sep 2013 • Stephen Bach, Bert Huang, Ben London, Lise Getoor
Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable.
no code implementations • NeurIPS 2012 • Stephen Bach, Matthias Broecheler, Lise Getoor, Dianne O'Leary
In this paper, we improve the scalability of MPE inference in a class of graphical models with piecewise-linear and piecewise-quadratic dependencies and linear constraints over continuous domains.
no code implementations • NeurIPS 2010 • Stephen Bach, Mark Maloof
To cope with concept drift, we placed a probability distribution over the location of the most-recent drift point.