no code implementations • CVPR 2024 • Mohammed Haroon Dupty, Yanfei Dong, Sicong Leng, Guoji Fu, Yong Liang Goh, Wei Lu, Wee Sun Lee
This paper addresses the challenge of object-centric layout generation under spatial constraints, seen in multiple domains including floorplan design process.
1 code implementation • 7 Aug 2023 • Guoji Fu, Mohammed Haroon Dupty, Yanfei Dong, Lee Wee Sun
We show how implicit GNN layers can be viewed as the fixed-point equation of a Dirichlet energy minimization problem and give conditions under which it may suffer from over-smoothing during training (OST) and inference (OSI).
no code implementations • 19 Jun 2023 • Huaisheng Zhu, Guoji Fu, Zhimeng Guo, Zhiwei Zhang, Teng Xiao, Suhang Wang
Graph Neural Networks (GNNs) have shown great power in various domains.
no code implementations • 23 May 2023 • Yuanfeng Ji, Yatao Bian, Guoji Fu, Peilin Zhao, Ping Luo
Firstly, SyNDock formulates multimeric protein docking as a problem of learning global transformations to holistically depict the placement of chain units of a complex, enabling a learning-centric solution.
no code implementations • 20 May 2022 • Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian, Hengtong Zhang, Chaochao Chen, Chengbin Hou, Guoji Fu, Liang Chen, Tingyang Xu, Yu Rong, Xiaolin Zheng, Junzhou Huang, Ran He, Baoyuan Wu, Guangyu Sun, Peng Cui, Zibin Zheng, Zhe Liu, Peilin Zhao
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
no code implementations • 15 Feb 2022 • Jintang Li, Bingzhe Wu, Chengbin Hou, Guoji Fu, Yatao Bian, Liang Chen, Junzhou Huang, Zibin Zheng
Despite the progress, applying DGL to real-world applications faces a series of reliability threats including inherent noise, distribution shift, and adversarial attacks.
1 code implementation • 2 Feb 2022 • Yifan Hou, Guoji Fu, Mrinmaya Sachan
We conduct experiments to verify that our GCS can indeed be used to correctly interpret the KI process, and we use it to analyze two well-known knowledge-enhanced LMs: ERNIE and K-Adapter, and find that only a small amount of factual knowledge is integrated in them.
2 code implementations • 14 Nov 2021 • Guoji Fu, Peilin Zhao, Yatao Bian
Graph neural networks (GNNs) have demonstrated superior performance for semi-supervised node classification on graphs, as a result of their ability to exploit node features and topological information simultaneously.
3 code implementations • 30 May 2021 • Chengbin Hou, Guoji Fu, Peng Yang, Zheng Hu, Shan He, Ke Tang
It is natural to ask if existing DNE methods can perform well for an input dynamic network without smooth changes.
1 code implementation • 8 Jun 2020 • Guoji Fu, Yifan Hou, Jian Zhang, Kaili Ma, Barakeel Fanseu Kamhoua, James Cheng
This paper aims to provide a theoretical framework to understand GNNs, specifically, spectral graph convolutional networks and graph attention networks, from graph signal denoising perspectives.
1 code implementation • 15 Feb 2019 • Guoji Fu, Chengbin Hou, Xin Yao
To tackle this issue, we propose a new NRL framework, named HSRL, to help existing NRL methods capture both the local and global topological information of a network.
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1 code implementation • 29 Jan 2019 • Guoji Fu, Bo Yuan, Qiqi Duan, Xin Yao
Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space.
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