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Heterogeneous transfer learning with applications
Date: 2017/2/13             Browse: 688
Seminar Topic: Heterogeneous transfer learning with applications

Speaker: Zhou Tianyi
Time: Feb. 14, 10:00 a.m. - 11:00 a.m.
Venue:  Room 1A-200, SIST Building

In many real-world problems, it is often time-consuming and expensive to collect labeled data. To alleviate this challenge, transfer learning (TL) techniques that adapt a model from a related task with ample labeled data to a task of interest with little or no additional human supervision have been proposed in recent years. Most TL methods assume that the data come from different domains having the same feature space and dimensionality. However, the assumptions may also be violated in some real world applications such as text-based image classification, cross-language document classification, and cross system recommendation. To handle situations when the assumptions do not hold, new TL approaches that utilize heterogeneous feature spaces are needed to solve the heterogeneous transfer learning (HTL) problem. In this talk, we will discuss how to learn good feature represention for the HTL.


Zhou Tianyi is a senior research engineer with SONY US Research Center in silicon valley. He is currently involving autonomous driving project in SONY. Prior to moving USA, he was a scientist with the Institute of High Performance Computing (IHPC) in Agency for Science, Technology and Research (A*STAR), Singapore. He received the Ph.D. degree in computer science from NTU, Singapore, in 2015. He has published 10+ research papers in renowned venues, including AAAI, IJCAI, CVPR, ECCV, TIP etc. He received the Best Poster Award Honorable Mention at ACML 2012 and Best Paper Award at BeyondLabeler workshop on IJCAI 2016. His current research interests include transfer learning, deep learning and its applications to text classification and computer vision problems.

Seminar 16097