no code implementations • 3 Nov 2023 • Zhuohang Dang, Minnan Luo, Chengyou Jia, Guang Dai, Jihong Wang, Xiaojun Chang, Jingdong Wang, Qinghua Zheng
Encoding only the task-related information from the raw data, \ie, disentangled representation learning, can greatly contribute to the robustness and generalizability of models.
no code implementations • 29 Nov 2022 • Zhuohang Dang, Jihong Wang, Minnan Luo, Chengyou Jia, Caixia Yan, Qinghua Zheng
To these challenges, we propose a novel Information Bottleneck (IB) based Disentangled Generation Framework for FSL, termed as DisGenIB, that can simultaneously guarantee the discrimination and diversity of generated samples.
1 code implementation • 20 May 2022 • Qinghua Zheng, Jihong Wang, Minnan Luo, YaoLiang Yu, Jundong Li, Lina Yao, Xiaojun Chang
Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the input graph is the most crucial to decide the model's decision?}"
no code implementations • 21 Jan 2022 • Jihong Wang, Minnan Luo, Jundong Li, Ziqi Liu, Jun Zhou, Qinghua Zheng
Our RGIB attempts to learn robust node representations against adversarial perturbations by preserving the original information in the benign graph while eliminating the adversarial information in the adversarial graph.
no code implementations • 22 Dec 2020 • Shiqi Sheng, Haijun Yang, Liuhua Mu, Zixin Wang, Jihong Wang, Peng Xiu, Jun Hu, Xin Zhang, Feng Zhang, Haiping Fang
We experimentally demonstrated that the AYFFF self-assemblies adsorbed with various monovalent cations (Na+, K+, and Li+) show unexpectedly super strong paramagnetism.
Biological Physics
1 code implementation • 22 Apr 2020 • Jihong Wang, Minnan Luo, Fnu Suya, Jundong Li, Zijiang Yang, Qinghua Zheng
Recent studies have shown that graph convolution networks (GCNs) are vulnerable to carefully designed attacks, which aim to cause misclassification of a specific node on the graph with unnoticeable perturbations.