no code implementations • 26 Apr 2024 • Xindi Zheng, Yuwei Wu, Yu Pan, WanYu Lin, Lei Ma, Jianjun Zhao
The crux of our work is that it admits both global and local representations of the input graph signal, which can capture the long-range dependencies.
no code implementations • 19 Apr 2024 • Zhenzhong Wang, Qingyuan Zeng, WanYu Lin, Min Jiang, Kay Chen Tan
While graph neural networks (GNNs) have become the de-facto standard for graph-based node classification, they impose a strong assumption on the availability of sufficient labeled samples.
no code implementations • 1 Apr 2024 • Haokai Hong, WanYu Lin, Kay Chen Tan
These structure variations are encoded with an equivariant encoder and treated as domain supervisors to control denoising.
1 code implementation • 22 Jun 2023 • Junjia Liu, Zhihao LI, WanYu Lin, Sylvain Calinon, Kay Chen Tan, Fei Chen
Soft object manipulation tasks in domestic scenes pose a significant challenge for existing robotic skill learning techniques due to their complex dynamics and variable shape characteristics.
1 code implementation • 15 Apr 2023 • Zihang Xiang, Tianhao Wang, WanYu Lin, Di Wang
In contrast, we leverage the random noise to construct an aggregation that effectively rejects many existing Byzantine attacks.
1 code implementation • 13 Mar 2023 • Cong Wang, Jinshan Pan, WanYu Lin, Jiangxin Dong, Xiao-Ming Wu
For this purpose, we develop a prompt based on the features of depth differences between the hazy input images and corresponding clear counterparts that can guide dehazing models for better restoration.
no code implementations • 7 Oct 2022 • Hao Wang, WanYu Lin, Hao He, Di Wang, Chengzhi Mao, Muhan Zhang
Recent years have seen advances on principles and guidance relating to accountable and ethical use of artificial intelligence (AI) spring up around the globe.
1 code implementation • CVPR 2022 • WanYu Lin, Hao Lan, Hao Wang, Baochun Li
This paper proposes a new eXplanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors.
no code implementations • 23 Jan 2022 • WanYu Lin, Baochun Li, Cong Wang
It is typical to collect these local views of social graphs and conduct graph learning tasks.
no code implementations • 29 Sep 2021 • WanYu Lin, Hao Lan, Hao Wang, Baochun Li
This paper proposes a new explanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors.
1 code implementation • 14 Apr 2021 • WanYu Lin, Hao Lan, Baochun Li
Specifically, we formulate the problem of providing explanations for the decisions of GNNs as a causal learning task.