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Journal of Computer Science and Technology (JCST, IF: 1.506)

Call for Papers --- Special Section on "Learning from Small Samples"
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AIMS AND SCOPE
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Machine learning has achieved great success in various tasks. With the rapid growth of model size as in deep networks, the learning models become more and more complex, typically requiring a large scale of training samples with label annotations. However, in real world applications, labeled data is usually limited. And it could be rather expensive to collect more labeled data because the labeling process is time consuming and requires domain expertise.

As a consequence, it becomes a major challenge to learn from a dataset with only a small amount of labeled samples. Currently, main solutions include: 1) utilizing the plentiful unlabeled data from the same data distribution, such as semi-supervised learning and weakly-supervised learning; 2) acquiring more labeled data with an annotation budget, such as active learning; 3) exploiting information from other related tasks, such as unsupervised and supervised pretraining, transfer learning, meta-learning and multi-task learning.

This special section of JCST journal papers will focus on new technologies and solutions related, but not limited to:

- Learning with weak supervision, including semi-supervised learning, active learning, multi-instance learning, etc.

- Learning by exploiting information from other tasks, including transfer learning, domain adaptation, meta-learning, multi-task learning, etc.

- Learning with very few training examples, including zero-shot learning, few-shot learning, etc.

- Application studies of learning from small samples.

Besides original research papers, we also strongly encourage high-quality survey papers, systems papers, and applications papers.


SCHEDULE
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- Manuscript Submission: October 20, 2020
- First Revision/Reject Notification: December 10, 2020
- Final Decision: February 15, 2021
- Camera-Ready: February 25, 2021
- Publication: May 2021


SUBMISSION PROCEDURE
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All submissions must be done electronically through JCST's e-submission system at:

https://mc03.manuscriptcentral.com/jcst

with a manuscript type: "Special Section on Learning from Small Samples".


LEADING EDITOR
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- Min-Ling Zhang (Southeast University, China)


GUEST EDITOR
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- Sheng-Jun Huang (Nanjing University of Aeronautics and Astronautics, China)
- Mingsheng Long (Tsinghua University, China)