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This work presents a novel, training-free pipeline that fundamentally reimagines this paradigm by explicitly decoupling 3D geometric reconstruction from semantic integration, and delivers an order-of-magnitude reduction in memory usage compared to SOTA baselines.
This work proposes a unified mathematical framework capable of capturing varying degrees of complexity across temporal graphs that is flexible and expressive enough to accommodate a wide range of network structures and temporal dynamics.
This study systematically benchmark six ranking loss functions, including state-of-the-art listwise methods, and five types of molecular representations across two large-scale drug screening datasets, CTRP and PRISM, to demonstrate that listwise loss functions such as LambdaLoss and LambdaRank consistently excel in bot...
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