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Found 3 papers, 1 papers with code
Structural Re-weighting Improves Graph Domain Adaptation
This work examines different impacts of distribution shifts caused by either graph structure or node attributes and identifies a new type of shift, named conditional structure shift (CSS), which current GDA approaches are provably sub-optimal to deal with.
Physics Community Needs, Tools, and Resources for Machine Learning
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges.
Semi-supervised Graph Neural Network for Particle-level Noise Removal
The graph neural network is trained on charged particles with well-known labels, which can be obtained from simulation truth information or measurements from data, and inferred on neutral particles of which such labeling is missing.
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