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Found 4 papers, 4 papers with code
Knodle: Modular Weakly Supervised Learning with PyTorch
Strategies for improving the training and prediction quality of weakly supervised machine learning models vary in how much they are tailored to a specific task or integrated with a specific model architecture.
ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision
A cost-effective alternative to manual data labeling is weak supervision (WS), where data samples are automatically annotated using a predefined set of labeling functions (LFs), rule-based mechanisms that generate artificial labels for the associated classes.
ACTC: Active Threshold Calibration for Cold-Start Knowledge Graph Completion
In this paper, we attempt for the first time cold-start calibration for KGC, where no annotated examples exist initially for calibration, and only a limited number of tuples can be selected for annotation.
Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal
Instead of cleaning the dataset prior to model training, the dataset is dynamically adjusted during the training process.
