Positive Unlabeled (PU) learning
PulseAugur coverage of Positive Unlabeled (PU) learning — every cluster mentioning Positive Unlabeled (PU) learning across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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New MINT framework enhances financial anomaly detection with graph neural networks
Researchers have developed MINT, a new graph neural network framework designed to improve financial anomaly detection. MINT addresses challenges like structural inconsistency, where malicious actors create deceptive con…
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New PUe Framework Enhances Learning with Biased Datasets · 2 sources tracked
Researchers have developed a new framework called PUe to enhance Positive-Unlabeled (PU) learning by addressing selection bias in real-world datasets. This framework, building on prior work by Bekker et al., introduces …
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New SPUNA framework detects covariate shift using weaker supervision
Researchers have developed a new framework called Spectral PU Neighborhood Annotation (SPUNA) to detect covariate shift in computer vision systems. This geometry-aware approach uses Positive Unlabeled (PU) learning, req…