Hierarchical Active Learning With Qualitative Feedback on Regions-Reference-Cited by-同舟云学术

Hierarchical Active Learning With Qualitative Feedback on Regions

Published:2023-06 Issue:3 Volume:53 Page:581-589
ISSN:2168-2291
Container-title:IEEE Transactions on Human-Machine Systems
language:
Short-container-title:IEEE Trans. Human-Mach. Syst.

Author:

Luo Zhipeng¹^ORCID,He Yazhou²,Xue Yanbing³,Wang Hongjun¹^ORCID,Hauskrecht Milos⁴^ORCID,Li Tianrui⁵^ORCID

Affiliation:

1. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

2. Department of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China

3. Walmart eCommerce, Sunnyvale, CA, USA

4. Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA

5. Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory, Sichuan, China

Funder

National Institutes of Health of the U.S.

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Human-Computer Interaction,Signal Processing,Control and Systems Engineering,Human Factors and Ergonomics

Link

http://xplorestaging.ieee.org/ielx7/6221037/10146026/10079190.pdf?arnumber=10079190

Reference47 articles.

1. Facial action coding system;ekman;Environmental Psychology and Nonverbal Behavior,0

2. A Survey of Deep Active Learning

3. Asking Generalized Queries to Domain Experts to Improve Learning

4. Ask me better questions

5. Deep Bayesian active learning with image data;gal;Proc Int Conf Mach Learn,0