Deep Learning - Some Thinking

发布日期:2017-01-04浏览次数:10


题  目:Deep Learning - Some Thinking

报告人:王熙照

深圳大学教授,IEEE Fellow

时  间16日上午10:00

地  点:南湖校区信控学院A311


信息与控制工程学院


报告摘要:Deep learning refers to an approach to training a multiple hidden layer feed forward neural network. Since it combines the supervised learning together with unsupervised learning, it demonstrates an excellent performance in many application domains and then becomes a really hot topic in the recent decade. This talk would like to give some guidelines about not only what advantages or improvements deep learning can achieve but also why. Some key issues in the deep learning are discussed, and some open problems are proposed for further investigating the deep learning.


报告人简介:

 Prof. Wang’s major research interests include uncertainty modeling and machine learning for big data. Prof. Wang has edited 6+ special issues and published 3 monographs, 2 textbooks, and 200+ peer-reviewed research papers. The H-index is 30 up to December2016. Prof. Wang is on the list of Elsevier 2015 most cited Chinese authors. As a Principle Investigator (PI) or co-PI, Prof. Wang has completed 30+ research projects. Prof. Wang is an IEEE Fellow, the previous BoG member of IEEE SMC society, the chair of IEEE SMC Technical Committee on Computational Intelligence, and the Chief Editor of International Journal of Machine Learning and Cybernetics.




1