GAUSS: GrAph-customized Universal Self-Supervised Learning-Reference-Cited by-同舟云学术

GAUSS: GrAph-customized Universal Self-Supervised Learning

Published:2024-05-13 Issue: Volume:2020 Page:582-593
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Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Yang Liang¹^ORCID,Hu Weixiao¹^ORCID,Xu Jizhong¹^ORCID,Shi Runjie¹^ORCID,He Dongxiao²^ORCID,Wang Chuan³^ORCID,Cao Xiaochun⁴^ORCID,Wang Zhen⁵^ORCID,Niu Bingxin⁶^ORCID,Guo Yuanfang⁷^ORCID

Affiliation:

1. School of Artificial Intelligence, Hebei University of Technology, Tianjin, China

2. College of Intelligence and Computing, Tianjin University, Tianjin, China

3. State Key Laboratory of Information Security, Institute of Information Engineering, Beijing, China

4. School of Cyber Science and Technology, Shenzhen Campus, Sun Yat-Sen University, Shenzhen, China

5. OPtics and ElectroNics (iOPEN), School of Cybersecurity, Northwestern Polytechnical University, Xi'an, China

6. School of Artificial Intelligence, Hebei University of Techonology, Tianjin, China

7. School of Computer Science and Engineering, Beihang University, Beijing, China

Funder

National Science Fund for Distinguished Young Scholars

National Social Science Fund of China

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province of China

Tencent Foundation and XPLORER PRIZE

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645453

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3. Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In ICML 2020. 1597--1607.

4. Eli Chien Jianhao Peng Pan Li and Olgica Milenkovic. 2021. Adaptive Universal Generalized PageRank Graph Neural Network. In ICLR.

5. Sparse Subspace Clustering: Algorithm, Theory, and Applications