Python codes for weakly-supervised learning
Positive and Unlabeled Materials Machine Learning (pumml) is a code that uses semi-supervised machine learning to classify materials from only positive and unlabeled examples.
🍊 PAUSE (Positive and Annealed Unlabeled Sentence Embedding), accepted by EMNLP'2021 🌴
uPU, nnPU and PN learning with Extra Trees classifier.
NeurIPS'20 Paper: "Learning from Positive and Unlabeled Data with Arbitrary Positive Shift"
Predicting protein functions using positive-unlabeled ranking with ontology-based priors
A template for a PU Bagging approach. PU bagging is effective when reliable negatives can't be identified in unlabeled data. Bootstrapping creates resampled subsets, helping the model distinguish true positives from true negatives. This process infers the negative class distribution, improving classification and model robustness.
Domain Adaptation with Dynamic Open-Set Targets
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