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CURE is proposed, a confounder-aware framework for multi-view urban region representation learning that improves predictive performance, remains robust under missing and noisy input views, and provides reliable cross-view integration through shared component separation and context-dependent view weighting.
This work proposes REFINE, a simple yet effective Representation lEarning Framework that tightly couples road-network-aware generative reconstruction with feedback-driven contrastive learning, enabling the model to capture fine-grained local movement semantics and global spatio-temporal dependencies without manually de...
DGCPath is presented, an innovative Distribution-aware Generative Contrastive learning framework for Path representation that establishes a synergistic connection between generative modeling and distributional contrastive learning, enabling the acquisition of robust and transferable feature embeddings.
The notion of a crystal supercell is introduced, which enables more comprehensive explorations of crystal structure and offers evidence that SILVA is capable of outperforming state-of-the-art methods.
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