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Peter L. Reichertz Institute for Medical Informatics, Hannover Medical School, Hannover, Germany, Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine (CAIMed), Hannover, Germany
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Ditch the global alignment: ST-Sheaf GNN learns dynamic, localized information flow for spatio-temporal forecasting, outperforming state-of-the-art methods by explicitly modeling latent local structure.
By explicitly disentangling redundant, unique, and synergistic information, COrAL achieves more stable and comprehensive multimodal embeddings compared to existing contrastive learning methods.