2 papers
cs.LG2026
GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models
Guangning Xu, Baoquan Zhang, Michael. K. Ng
Parameter-efficient fine-tuning (PEFT) has emerged as a resource-efficient strategy for adapting Pretrained Foundation Models (PFMs) by learning a small number of task-specific upd…
cs.LG2024
Core Knowledge Learning Framework for Graph Adaptation and Scalability Learning
Bowen Zhang, Zhichao Huang, Genan Dai +3
Graph classification is a pivotal challenge in machine learning, especially within the realm of graph-based data, given its importance in numerous real-world applications such as s…