no code implementations • 26 Oct 2023 • Shuai Zheng, Zhizhe Liu, Zhenfeng Zhu, Xingxing Zhang, JianXin Li, Yao Zhao
On this basis, BiKT not only allows us to acquire knowledge from both the GNN and its derived model but promotes each other by injecting the knowledge into the other.
1 code implementation • 7 Dec 2022 • Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu, Youru Li, Yao Zhao
Graph neural networks (GNNs) have shown remarkable performance on homophilic graph data while being far less impressive when handling non-homophilic graph data due to the inherent low-pass filtering property of GNNs.
1 code implementation • 11 Mar 2022 • Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu, Zhenyu Guo, Yang Liu, Yuchen Yang, Yao Zhao
For disease prediction tasks, most existing graph-based methods tend to define the graph manually based on specified modality (e. g., demographic information), and then integrated other modalities to obtain the patient representation by Graph Representation Learning (GRL).
no code implementations • 19 Jul 2021 • Zhenyu Guo, Shuai Zheng, Zhizhe Liu, Kun Yan, Zhenfeng Zhu
Treatment effect estimation, which refers to the estimation of causal effects and aims to measure the strength of the causal relationship, is of great importance in many fields but is a challenging problem in practice.
no code implementations • 1 Jul 2021 • Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu, Zhenyu Guo, Yang Liu, Yao Zhao
However, it is not easy for these approaches to generalize to unseen samples.
1 code implementation • 15 Mar 2021 • Zhizhe Liu, Zhenfeng Zhu, Shuai Zheng, Yang Liu, Jiayu Zhou, Yao Zhao
To bridge the gap between the source and target domains in unsupervised domain adaptation (UDA), the most common strategy puts focus on matching the marginal distributions in the feature space through adversarial learning.
1 code implementation • 12 Mar 2021 • Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu, Jian Cheng, Yao Zhao
For them, a component-specific aggregation approach is proposed to achieve micro-disentanglement by inferring latent components that cause the links between nodes.
no code implementations • 2 Oct 2020 • Zhizhe Liu, Xingxing Zhang, Zhenfeng Zhu, Shuai Zheng, Yao Zhao, Jian Cheng
There have been numerous methods proposed for human identification, such as face identification, person re-identification, and gait identification.
1 code implementation • CVPR 2020 • Shuai Zheng, Zhenfeng Zhu, Xingxing Zhang, Zhizhe Liu, Jian Cheng, Yao Zhao
Graph representation learning aims to encode all nodes of a graph into low-dimensional vectors that will serve as input of many compute vision tasks.
Generative Adversarial Network Graph Representation Learning
no code implementations • 22 Oct 2019 • Zhizhe Liu, Xingxing Zhang, Zhenfeng Zhu, Shuai Zheng, Yao Zhao, Jian Cheng
The key to ZSL is to transfer knowledge from the seen to the unseen classes via auxiliary class attribute vectors.