1 code implementation • 13 Mar 2024 • Weikai Li, Zhiping Xiao, Xiao Luo, Yizhou Sun
We propose a new method of evaluating node influence, which measures the prediction change of a trained GNN model caused by removing a node.
1 code implementation • 30 Nov 2023 • Weikai Li, Hongfeng Wei, Yanlai Wu, Jie Yang, Yudi Ruan, Yuan Li, Ying Tang
Few-shot object detection (FSOD) aims to extract semantic knowledge from limited object instances of novel categories within a target domain.
no code implementations • 15 Nov 2023 • Hao Peng, Xiaozhi Wang, Jianhui Chen, Weikai Li, Yunjia Qi, Zimu Wang, Zhili Wu, Kaisheng Zeng, Bin Xu, Lei Hou, Juanzi Li
In this paper, we find that ICL falls short of handling specification-heavy tasks, which are tasks with complicated and extensive task specifications, requiring several hours for ordinary humans to master, such as traditional information extraction tasks.
1 code implementation • 15 Jun 2023 • Jifan Yu, Xiaozhi Wang, Shangqing Tu, Shulin Cao, Daniel Zhang-li, Xin Lv, Hao Peng, Zijun Yao, Xiaohan Zhang, Hanming Li, Chunyang Li, Zheyuan Zhang, Yushi Bai, Yantao Liu, Amy Xin, Nianyi Lin, Kaifeng Yun, Linlu Gong, Jianhui Chen, Zhili Wu, Yunjia Qi, Weikai Li, Yong Guan, Kaisheng Zeng, Ji Qi, Hailong Jin, Jinxin Liu, Yu Gu, Yuan YAO, Ning Ding, Lei Hou, Zhiyuan Liu, Bin Xu, Jie Tang, Juanzi Li
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations.
1 code implementation • 28 May 2022 • Ziang Li, Ming Ding, Weikai Li, Zihan Wang, Ziyu Zeng, Yukuo Cen, Jie Tang
graph benchmark (IGB) consisting of 4 datasets.
no code implementations • 7 Apr 2022 • Weikai Li, Meng Cao, Songcan Chen
Unsupervised Source (data) Free domain adaptation (USFDA) aims to transfer knowledge from a well-trained source model to a related but unlabeled target domain.
1 code implementation • 29 Aug 2021 • Weikai Li, Songcan Chen
Considering the difficulty of perfect alignment in solving PDA, we turn to focus on the model smoothness while discard the riskier domain alignment to enhance the adaptability of the model.
no code implementations • 29 Apr 2021 • Weikai Li, Yongxiang Tang, Zhengxia Wang, Shuo Hu, Xin Gao
We aim to establish an individual metabolic connectome method to characterize the aberrant connectivity patterns and topological alterations of the individual-level brain metabolic connectome and their diagnostic value in PD.
1 code implementation • 24 Dec 2020 • Weikai Li, Chuanxing Geng, Songcan Chen
On the one hand, for small data cases, CV suffers a conservatively biased estimation, since some part of the limited data has to hold out for validation.
1 code implementation • 1 Sep 2020 • Weikai Li, Songcan Chen
Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain by transferring knowledge from labeled source domain with domain shift.