Call for Papers - SI on WSLCCT

Special Issue on Weakly Supervised Learning for Computational and Cognitive Tasks

Aims and Scope

Many computational and cognitive tasks have difficulties to collect sufficient well-annotated data for supervised model training, because annotated data usually need a human expert even special equipment and cost a long time to annotate manually. In these circumstances, weakly supervised learning recently has been emerging as an effective tactic. Comparing to supervised learning, weakly supervised learning could train a model using less annotated data and/or human intervention. This special issue calls for recent advances in weakly supervised learning research in the context of computational and cognitive tasks.

Lead Guest Editor

Prof. Enmei Tu
Crown Research Institute, New Zealand
Research Interests: Semi-Supervised Learning, Neural Networks & Brain Cognition Theory Inspired Learning

Guest Editors

Prof. Wei Liu
Shanghai Jiao Tong University, China
Research Interests: Machine Learning, Deep learning, EEG-based Emotion Recognition

Dr. Yaqian Zhang
The University of Waikato, New Zealand
Research Interests: Reinforcement Learning, Continual learning, Machine Learning, Human-Computer Interaction

Dr. Lin Zhu
University of Shanghai for Science and Technology, China
Research Interests: Bioinformatics, Computational Biology, Fuzzy Clustering.

Special Issue Information

We encourage researchers and experts worldwide to contribute by submitting high-quality original research papers and review papers. 

This special issue mainly includes (but not limited to) the following topics:

· Semi-supervised learning and self-supervised learning theory, algorithm and their applications
· One-Class learning/ Positive-Unlabelled (PU) learning theory, algorithm and their applications
· Transfer learning, self-adaptive learning, active learning, few (zero) shot learning and their applications
· Training deep/shallow models with noisy labels, partial labels, imbalanced labels and additional out-of-distribution labels, etc.
· Continual learning for sequential tasks, streaming tasks with limited/restricted data access

Manuscript Submission Information

Submission deadline: 1 April 2023

Submissions that pass pre-check will be reviewed by at least two reviewers of the specific field. Accepted papers will be published on early access first and sent for copy editing and typesetting. Then all papers will be included in the special issue when it is published.

If you have any queries regarding the special issue or other matters, please feel free to contact the editorial office: /