OMG: Towards Effective Graph Classification Against Label Noise-Reference-Cited by-同舟云学术

OMG: Towards Effective Graph Classification Against Label Noise

Published:2023-12-01 Issue:12 Volume:35 Page:12873-12886
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Yin Nan¹^ORCID,Shen Li²^ORCID,Wang Mengzhu³^ORCID,Luo Xiao⁴^ORCID,Luo Zhigang³^ORCID,Tao Dacheng⁵^ORCID

Affiliation:

1. Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

2. JD Explore Academy, Beijing, China

3. National University of Defense Technology, Changsha, Hunan, China

4. University of California, Los Angeles, CA, USA

5. University of Sydney, Camperdown, NSW, Australia

Funder

Science and Technology Innovation 2030

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10311056/10113198.pdf?arnumber=10113198

Reference77 articles.

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4. Expectation pooling: an effective and interpretable pooling method for predicting DNA–protein binding

5. Understanding deep learning (still) requires rethinking generalization

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