Heterogeneous Transfer Learning with Applications
In many real-world problems, it is often time-consuming and expensive to collect and 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.
About the speaker
Zhou Tianyi is a senior research engineer with SONY US Research Center in silicon valley. Prior to moving to 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. 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.
|LARC is supported by the National Research Foundation, Prime Minister's Office, Singapore under its International Research Centres in Singapore Funding Initiative.|
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