2 citations · 4 across the 13 of their papers we have counts for
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cs.CV2023★ 1 cited
Parameter-efficient is not sufficient: Exploring Parameter, Memory, and Time Efficient Adapter Tuning for Dense Predictions
Dongshuo Yin, Xueting Han, Bin Li +2
Pre-training & fine-tuning is a prevalent paradigm in computer vision (CV). Recently, parameter-efficient transfer learning (PETL) methods have shown promising performance in adapt…
cs.LG2023
Time-aware Graph Structure Learning via Sequence Prediction on Temporal Graphs
Haozhen Zhang, Xueting Han, Xi Xiao +1
Temporal Graph Learning, which aims to model the time-evolving nature of graphs, has gained increasing attention and achieved remarkable performance recently. However, in reality,…
cs.LG2023★ 2 cited
AdapterGNN: Parameter-Efficient Fine-Tuning Improves Generalization in GNNs
Shengrui Li, Xueting Han, Jing Bai
Fine-tuning pre-trained models has recently yielded remarkable performance gains in graph neural networks (GNNs). In addition to pre-training techniques, inspired by the latest wor…