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Found 2 papers, 1 papers with code
A General Framework for Learning under Corruption: Label Noise, Attribute Noise, and Beyond
Corruption is frequently observed in collected data and has been extensively studied in machine learning under different corruption models.
The interplay between ranking and communities in networks
It assumes that each node has a preference in the interaction mechanism and nodes with the same preference are more likely to interact, while heterogeneous interactions are still allowed.
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