default author photo

Ziyue Qiao

Hong Kong University of Science & Technology (HKUST)

  • Hong Kong University of Science & Technology (HKUST)

SCHOLARLY PAPERS

2

DOWNLOADS

238

TOTAL CITATIONS

0

Scholarly Papers (2)

Actions:
Email selected abstracts
1.

Focus on Informative Graphs! Semi-Supervised Active Learning for Graph-Level Classification

Downloads 226 (337,699)

Focus on Informative Graphs! Semi-Supervised Active Learning for Graph-Level Classification

Number of pages: 12 Posted: 06 Apr 2023
Wei Ju, Xiao Luo, Yifang Qin, Ziyue Qiao, Siyu Yi, Luchen Liu, Yanjie Fu and Ming Zhang
Peking University, University of California at Los Angeles, Peking University, Hong Kong University of Science & Technology (HKUST), Nankai University, Peking University, University of Central Florida and Peking University
Downloads 159 (469,070)

Abstract:

Loading...

Graph Classification, graph neural networks, semi-supervised learning, Active Learning

Focus on Informative Graphs! Semi-Supervised Active Learning for Graph-Level Classification

Number of pages: 30 Posted: 24 May 2023
Wei Ju, Xiao Luo, Yifang Qin, Ziyue Qiao, Siyu Yi, Luchen Liu, Yanjie Fu and Ming Zhang
Peking University, University of California at Los Angeles, Peking University, Hong Kong University of Science & Technology (HKUST), Nankai University, Peking University, University of Central Florida and Peking University
Downloads 67 (912,349)

Abstract:

Loading...

Graph Classification, Graph neural networks, Semi-supervised Learning, active learning

2.

A Composite Path and Answer Self-Scoring Reward Mechanism for Test-Time Reinforcement Learning

Number of pages: 19 Posted: 06 Feb 2026
Jingyu Xing, Tang Chenwei, Xinyu Liu, Deng Xiong, Shudong Huang, Wei Ju, Jiancheng Lv and Ziyue Qiao
Sichuan University, Sichuan University, Sichuan University, Stevens Institute of Technology, Sichuan University, Sichuan University, Sichuan University and Hong Kong University of Science & Technology (HKUST)
Downloads 12 (1,548,648)

Abstract:

Loading...

Reinforcement Learning, Test time adaption, pseudo label, calibration, Unsupervised Learning