7 papers
Parameter Interpolation Adversarial Training for Robust Image Classification
Xin Liu, Yichen Yang, Kun He +1
Though deep neural networks exhibit superior performance on various tasks, they are still plagued by adversarial examples. Adversarial training has been demonstrated to be the most…
Learning to Rewrite Prompts for Bootstrapping LLMs on Downstream Tasks
Qinhao Zhou, Xiang Xiang, Kun He +1
In recent years, the growing interest in Large Language Models (LLMs) has significantly advanced prompt engineering, transitioning from manual design to model-based optimization. P…
DAM-GT: Dual Positional Encoding-Based Attention Masking Graph Transformer for Node Classification
Chenyang Li, Jinsong Chen, John E. Hopcroft +1
Neighborhood-aware tokenized graph Transformers have recently shown great potential for node classification tasks. Despite their effectiveness, our in-depth analysis of neighborhoo…
Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse
Kun He, Zijian Song, Shuoxi Zhang +1
Class-Incremental Learning (CIL) is a critical capability for real-world applications, enabling learning systems to adapt to new tasks while retaining knowledge from previous ones.…
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers
Jinsong Chen, Hanpeng Liu, John E. Hopcroft +1
While tokenized graph Transformers have demonstrated strong performance in node classification tasks, their reliance on a limited subset of nodes with high similarity scores for co…
Rethinking Tokenized Graph Transformers for Node Classification
Jinsong Chen, Chenyang Li, GaiChao Li +2
Node tokenized graph Transformers (GTs) have shown promising performance in node classification. The generation of token sequences is the key module in existing tokenized GTs which…