39 citations · 64 across the 5 of their papers we have counts for
4 papers · 1 filter
GraphFM: A Comprehensive Benchmark for Graph Foundation Model
Yuhao Xu, Xinqi Liu, Keyu Duan +4
Foundation Models (FMs) serve as a general class for the development of artificial intelligence systems, offering broad potential for generalization across a spectrum of downstream…
Contrastive Knowledge Graph Error Detection
Qinggang Zhang, Junnan Dong, Keyu Duan +3
Knowledge Graph (KG) errors introduce non-negligible noise, severely affecting KG-related downstream tasks. Detecting errors in KGs is challenging since the patterns of errors are…
Transfer Learning Toolkit: Primers and Benchmarks
Fuzhen Zhuang, Keyu Duan, Tongjia Guo +4
The transfer learning toolkit wraps the codes of 17 transfer learning models and provides integrated interfaces, allowing users to use those models by calling a simple function. It…
A Comprehensive Survey on Transfer Learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan +5
Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this wa…