SUPT assigns prompt features at the subgraph level to enable universal prompt tuning for any GNN pre-training strategy and outperforms fine-tuning in 42 of 45 full-shot and 41 of 45 few-shot graph experiments with average gains of 2.5% and 6.6%.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2024 2verdicts
UNVERDICTED 2representative citing papers
CoreGuard introduces a computation- and communication-efficient protocol claimed to deliver upper-bound security against model stealing for edge-deployed LLMs with negligible overhead.
citing papers explorer
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Subgraph-level Universal Prompt Tuning
SUPT assigns prompt features at the subgraph level to enable universal prompt tuning for any GNN pre-training strategy and outperforms fine-tuning in 42 of 45 full-shot and 41 of 45 few-shot graph experiments with average gains of 2.5% and 6.6%.
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CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment
CoreGuard introduces a computation- and communication-efficient protocol claimed to deliver upper-bound security against model stealing for edge-deployed LLMs with negligible overhead.