2022 International Conference on Machine Learning and Intelligent Science
December 16-18 @ City University of Macau, Macau S.A.R, China
2022 International Conference on Machine Learning and Intelligent Science (MLIS
2022) will be held in
City University of Macau, Macau S.A.R, China as a workshop of ICKD during December 16-18, 2022. It aims to provide a forum for
researchers, practitioners, and professionals from both the
industry and the academia to share their newest research
findings and results.
MLIS is sponsored by City University of Macau, Macau S.A.R, China. The conference calls for high-quality, original research papers in the theory and practice of machine learning and intelligent science. The conference also solicits proposals focusing on frontier research, new ideas and paradigms in machine learning and intelligent technologies.
Due to the unpredictable and unstable situation of the pandemic, it's acceptable for those participants who can't attend the conference because of the travel restrictions to present their papers online with reduced registration fee. In the meanwhile, the organizing committee will monitor the situation and the conference may be switched to fully virtual mode if necessary.
|Submission Deadline||October 20, 2022 (Extended)|
|Acceptance Notification||November 10, 2022|
|Registration Deadline||November 20, 2022|
|Conference Dates||December 16-18, 2022|
Please submit your full paper via iConference submission system using the following link: Electronic Submission System; ( .pdf). Please submit only abstract if you plan on just presenting your paper without publication
Papers submitted to MLIS will be reviewed by both the conference committees and IJMLC editorial board, and accepted papers will be published in the International Journal of Machine Learning and Computing , which will be indexed by Inspec, Google Scholar, Crossref, CNKI etc.
If you're NOT looking to have your papers published in the above journal, it's acceptable to submit your abstracts to the conference just for oral presentation without publication.
Authors are invited to submit full papers describing original research work in areas including, but not limited to:
Computational Theories of Learning
Big Data Visualization
Multitasking and Transfer Learning