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cs.LG2026
Disentangled Graph Prompting for Out-Of-Distribution Detection
Cheng Yang, Yu Hao, Qi Zhang +1
When testing data and training data come from different distributions, deep neural networks (DNNs) will face significant safety risks in practical applications. Therefore, out-of-d…
cs.LG2026
Data-centric Prompt Tuning for Dynamic Graphs
Yufei Peng, Cheng Yang, Zhengjie Fan +1
Dynamic graphs have attracted increasing attention due to their ability to model complex and evolving relationships in real-world scenarios. Traditional approaches typically pre-tr…