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This paper proposes TraveL, a Transformer-based multi-view framework for learning distributional representations of paths in road networks, capturing varied traveler behaviors and regional correlations, and outperforming state-of-the-art methods in travel time estimation, path similarity, and destination prediction.
ProPRL introduces a property-aware framework for prerequisite relation learning in educational knowledge graphs, combining concept-resource hypergraph and directed behavior graph with adaptive pair-conditioned fusion and an irreversibility constraint to achieve state-of-the-art performance.
The paper studies interaction-aware mixture-of-experts for post-stroke rigidity prediction using multi-level views of structured health records, showing that while performance gains are minimal, routing attribution reveals systematic importance differences across views, highlighting view construction as key to interpretability.
This paper proposes a token-centric dual-view learning framework that unifies prompt-based adaptation and cross-view fusion within a frozen vision transformer to improve breast cancer classification from mammography images, achieving consistent improvements on VinDr-Mammo and CMMD datasets.
ConfSleepNet is a conflict-aware evidential framework for reliable sleep stage classification using multi-modal data. It introduces hybrid category structures and a conflict-aware aggregation method to resolve inter-view conflicts, demonstrating effectiveness on sleep staging tasks.