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Found 6 papers, 2 papers with code
Alfred: A System for Prompted Weak Supervision
Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting.
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
Training and evaluating language models increasingly requires the construction of meta-datasets --diverse collections of curated data with clear provenance.
Zero-Shot Learning with Common Sense Knowledge Graphs
We propose to learn class representations from common sense knowledge graphs.
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction
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.
Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization
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.
A Bayesian Approach to Concept Drift
To cope with concept drift, we placed a probability distribution over the location of the most-recent drift point.
