AdaTTA is an actor-critic RL framework that selects sequence-specific test-time augmentations and improves recommendation metrics by up to 26% over fixed augmentation strategies on four datasets.
Empowering graph rep- resentation learning with test-time graph transformation
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
S²PLR identifies a safe subspace for reliable pseudo-labels in source-free graph domain adaptation using semantic committee signals and structural contrastive verification, then applies noise-tolerant regularization to uncertain samples.
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
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Beyond One-Size-Fits-All: Adaptive Test-Time Augmentation for Sequential Recommendation
AdaTTA is an actor-critic RL framework that selects sequence-specific test-time augmentations and improves recommendation metrics by up to 26% over fixed augmentation strategies on four datasets.
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Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation
S²PLR identifies a safe subspace for reliable pseudo-labels in source-free graph domain adaptation using semantic committee signals and structural contrastive verification, then applies noise-tolerant regularization to uncertain samples.
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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.