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Found 3 papers, 1 papers with code
Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism
However, those post-hoc methods often fail to provide stable interpretation and may extract features that are spuriously correlated with the task.
Ranked #11 on
Graph Property Prediction
on ogbg-molhiv
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.
Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.
