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When Transfer Learning Meets Deep Learning
Professor Qiang Yang - Hong Kong University
Monday, November 27, 2017, 4:00-5:00 pm Calendar
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Deep learning has achieved great success as evidenced by many challenging applications. However, deep learning developed so far has some inherent limitations. In particular, deep learning is not yet adaptable to different domains and cannot handle small data. In this talk, I will give an overview of how transfer learning can help alleviate these problems.  In particular,  I will survey some recent progress on integrating deep learning and transfer learning together and show some interesting applications in sentiment analysis, image processing and dialog systems.



Qiang Yang is the head of Computer Science and Engineering Department at Hong Kong University of Science and Technology (HKUST), where he is a New Bright Endowed Chair Professor of Engineering and the founding director of HKUST’s Big Data Institute.  His research interest is artificial intelligence, including machine learning, data mining and planning.  He is a fellow of AAAI, IEEE, IAPR and AAAS.   He received his PhD from the Department of Computer Science at the University of Maryland, College Park in 1989 and had been a faculty member at the University of Waterloo between 1989 and 1995.  He was a professor and NSERC Industrial Research Chair at Simon Fraser University in Canada from 1995 to 2001.   He had been the founding director of the Huawei's Noah's Ark Research Lab between 2012 and 2015.  He was the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST) and the founding Editor in Chief of IEEE Transactions on Big Data (IEEE TBD).  He has served as a PC Chair or General Chair of several international conferences, including ACM KDD, IJCAI, RecSys, IUI and ICCBR.   In 2017, he received the ACM SIGKDD Distinguished Service Award. He is currently the President of IJCAI and a council member of AAAI.

This talk is organized by Adelaide Findlay