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Label Influence Propagation reveals that dynamically adjusting label influences can significantly enhance multi-label node classification performance, outperforming existing methods.
Achieving up to 10x weight compression in LLMs without altering weights could revolutionize GPU memory usage and model deployment efficiency.
A unified hardware acceleration framework for aggregation achieves notable efficiency gains while simplifying programming across diverse platforms.
