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Found 3 papers, 3 papers with code
Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength
Therefore, we firstly generate a dataset with 45, 000 numerical simulations and 900 particle types to facilitate the research progress of machine learning for particle crushing.
Transition Propagation Graph Neural Networks for Temporal Networks
The proposed TIP-GNN focuses on the bilevel graph structure in temporal networks: besides the explicit interaction graph, a node's sequential interactions can also be constructed as a transition graph.
Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation
Most existing online knowledge distillation(OKD) techniques typically require sophisticated modules to produce diverse knowledge for improving students' generalization ability.
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