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HKUST School of Engineering Distinguished Research Excellence Award Keynote
Artificial Intelligence Under Big Data and Small Data
Date : 14 Dec 2016 (Wed)
Time : 5:30pm - 6:30pm
Venue : Chiang Chen Lecture Theater (LT-J), HKUST
Tea Reception will be served after the keynote at the Engineering Commons
(2/F, Academic Concourse, Lifts 27-28)
Speaker : Prof Qiang Yang (楊強教授)
New Bright Professor of Engineering
Chair Professor and Head of Department of Computer Science and Engineering
School of Engineering Distinguished Research Excellence Award Awardee 2016
Registration : Please make registration on or before 9 Dec (Fri)
Enquiries : Please contact Karen Yau (2358-8446)
Abstract
Today, artificial intelligence techniques are often exploited to extract the value from Big Data. However, often we only have small data, and in such cases many machine learning based methods cannot be fully used. In this talk, I will give an overview of my research in transfer learning, which adapts a learned model from a big data to new situations where only small data might be available. I will discuss foundations and different transfer learning methods, as well as their applications in a number of application domains, including recommender systems, sentiment analysis and dialog systems.
 
About the Speaker
Qiang Yang is the New Bright Professor of Engineering and Chair Professor, as well as the Head of the Department of Computer Science and Engineering at HKUST. His research interests include Artificial Intelligence, Machine Learning and Data Mining. In 2012, he became the founding head of the Huawei Noah's Ark Research Lab (till 2015). He is the founding Editor in Chief of the IEEE Transactions on Big Data and had been the founding Editor in Chief of ACM Transactions on Intelligent Systems and Technology (ACM TIST). He is a trustee member of the International Joint Conference on Artificial Intelligence (IJCAI), and the Program Chair of IJCAI 2015 Conference. He had been keynote and invited speakers as well as Chairs for various top conferences, including ACM KDD, ACM RecSys, ACM IUI and ICCBR. He is a fellow of IEEE, AAAI, AAAS and IAPR.
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