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Graph Q-Learning for Combinatorial Optimization
Graph-structured data is ubiquitous throughout natural and social sciences, and Graph Neural Networks (GNNs) have recently been shown to be effective at solving prediction and inference problems on graph data.
Disentangled Neural Relational Inference for Interpretable Motion Prediction
The results show superior performance compared to existing methods in modeling spatio-temporal relations, motion prediction, and identifying time-invariant latent features.
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