In generalized contrastive learning with imbalanced classes, optimal representations collapse to class means whose angular geometry is determined by class proportions via convex optimization, and extreme imbalance causes all minority classes to collapse to one vector.
arXiv preprint arXiv:2010.02037 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
TEMG-TTA combines temporal motif-aware graph learning with test-time adaptation to improve OOD anomaly detection on blockchain graphs, reporting an average 54.88% gain over prior GAD methods on five real-world datasets.
citing papers explorer
-
Optimal Representations for Generalized Contrastive Learning with Imbalanced Datasets
In generalized contrastive learning with imbalanced classes, optimal representations collapse to class means whose angular geometry is determined by class proportions via convex optimization, and extreme imbalance causes all minority classes to collapse to one vector.
-
Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection
TEMG-TTA combines temporal motif-aware graph learning with test-time adaptation to improve OOD anomaly detection on blockchain graphs, reporting an average 54.88% gain over prior GAD methods on five real-world datasets.