Deep Learning for Knowledge Transfer
(DLKT 2020)

Expected Duration
(half-day)

About

Previous supervised learning algorithms mainly assume that there are plenty of i.i.d. sampled labeled data to train a good model for test data. However, this assumption does not always hold in real-world applications, since labeling data is time consuming and labor tedious. Furthermore, the test data are usually sampled from the distribution which is different from the one of training data. The advanced algorithms based on knowledge transfer or sharing provide an effective way to handle this issue, e.g., transfer learning, multi-task learning and multi-view learning, since they either try to handle the distribution mismatch problem or the shortage of labeled data.

In recent years, deep learning has been proved to have the ability to learn powerful representations for various kinds of tasks. On the one hand, although there are large amount of previous works based on knowledge transfer or sharing, there are only small amount of them applying deep learning techniques. In this workshop, we aim to bring researchers and practitioners who work on various aspects of advanced knowledge transfer algorithms based on deep learning techniques, to discuss on the state-of-the-art and open problems, to share their expertise and exchange the ideas, and to offer them an opportunity to identify new promising research directions.

DLKT 2020 will be co-held with ICDM 2020 in Sorrento, Italy.

Call for Papers

This workshop solicits papers whose topics fall into (but not limited to) the following categories:

  • Transfer learning based on deep learning techniques.
  • Multi-task learning based on deep learning techniques.
  • Multi-view learning based on deep learning techniques.
  • The applications of knowledge transfer and sharing algorithms in real worlds.
  • Knowledge transfer for zero-shot learning.
  • Applications of deep learning and knowledge transfer for recommendation systems.
  • Knowledge Transfer with Knowledge Graph

Submission Information

Paper submissions should be limited to a maximum of ten (10) pages (max 8 pages plus 2 extra pages) for peer review, in the IEEE 2-column format (link), including the bibliography and any possible appendices. Submissions longer than 10 pages will be rejected without review. All submissions will be triple-blind reviewed by the Program Committee on the basis of technical quality, relevance to scope of the deep learning for knowledge transfer workshop, originality, significance, and clarity. Submissions of a paper should be regarded as an undertaking that, should the paper be accepted, at least one author will attend the conference to present the work. By the unique ICDM tradition, all accepted workshop papers will be published in the dedicated ICDMW proceedings published by the IEEE Computer Society Press.

More instructions about submission guidelines is at http://icdm2020.bigke.org/

Authors should make the submissions through the Online Submission System.


Important Dates

  • All deadlines are at 11:59 PM Pacific Daylight Time.
  • Workshop Duration: half-day.
  • Workshop paper submissions deadline: August 24 2020.
  • Workshop paper notification: September 17, 2020.
  • Camera-ready deadline and copyright forms: September 24, 2020.
  • Workshops date: November 17, 2020

Organizers

“Fuzhen Zhuang is an associate professor in the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. His research interests include transfer learning, machine learning, data mining, multi-task learning and recommendation systems. He has published more than 100 papers in the prestigious refereed journals and conference proceedings, such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems, ACM Transactions on Intelligent Systems and Technology, Information Sciences, Neural Networks, SIGKDD, IJCAI, AAAI, WWW, ICDE, ACM CIKM, ACM WSDM, SIAM SDM and IEEE ICDM.”

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Fuzhen Zhuang, zhuangfuzhen@ict.ac.cn
Institute of Computing Technology, Chinese Academy of Sciences

“Deqing Wang, PhD，is an associate professor at School of Computer Science，Beihang University， and the Chief Engineer of National Engineering Research Center for Science and Technology Resource Sharing Service. He was the visiting scholar at Rutgers University (USA) From Nov. 2016 to Nov. 2017. His main research interesting includes knowledge graph, machine learning and scientific big data analysis and mining. He has published more than 30 papers.”

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Deqing Wang
dqwang@buaa.edu.cn, Beihang university

“Pengpeng Zhao received the Ph.D. degree in computer science from Soochow University in 2008. He is a Professor in the School of Computer Science and Technology at Soochow University. He has published more than 60 papers in prestigious international conferences and journals, including ACM MM, AAAI, IJCAI, ICDM, CIKM, DASFAA, and ICME. He was a Program Committee Member of international conferences, such as AAAI, IJCAI, CIKM, DASFAA, and PAKDD. His current research interests include data mining, deep learning, big data analysis, and recommender systems.”

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Pengpeng Zhao
ppzhao@suda.edu.cn, Soochow University

PC Program Committee

  • Xiang Ao, aoxiang@ict.ac.cn
  • Chuan Shi, shichuan@bupt.edu.cn
  • Di Jin, jindi@tju.edu.cn
  • Zhongying Zhao, zzysuin@163.com
  • Shujuan Ji, jsjsuzie@sina.com
  • Weizhong Zhao, wzzhao@mail.ccnu.edu.cn
  • Jiajie Xu, xujj@suda.edu.cn
  • Jingjing Gu, gujingjing@nuaa.edu.cn
  • Jie Liu, Jliu@nankai.edu.cn
  • Guoxian Yu, gxyu@swu.edu.cn
  • Yimin Wen, ymwen2004@aliyun.com
  • Yang Liu, yangliuyl@tju.edu.cn
  • Guoliang He, glhe@whu.edu.cn