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Lifelong Machine Learning Literature Survey
PhD Qualifying Examination Title: "Lifelong Machine Learning Literature Survey" by Mr. Lianghao LI Abstract: In the past decades, there have been significant advances in machine learning theory and algorithms. However, there is a big missing part in the most of learning algorithms proposed in the past decades if we compare them to how human learn to solve problems. That is the human's continuously study capability. Most of the machine learning theory and algorithms still focus on solving the problem for one particular task, such as text classification, image classification, image segmentation, etc. But human learns to solve various problems one after the other, continuously. For example, a musician might learn how to play many different instruments, studies how to compose and perform different music year over year. Because of the continuously study capability, that musician should be able to learn how to play guitar very quick if she already knows how to play piano and compose music. We call a machine learning system which has this kind of continuously study capability a lifelong learning system. Another important reason that why lifelong learning will be a key area in machine learning is that a large amount of labeled data from a large number of tasks become available. That is largely driven by the prevalence of deep learning which requires a lot of labeled data to learn more complex models with deep architectures. The huge amount of labeled data from many different tasks unlock lifelong learning system research and also push the community to start thinking about beyond the traditional one-hot model training. In this survey, we review in detail about the lifelong learning paradigm and related works. Date: Monday, 14 May 2018 Time: 3:00pm - 5:00pm Venue: Room 3494 Lifts 25/26 Committee Members: Prof. Qiang Yang (Supervisor) Prof. Dit-Yan Yeung (Chairperson) Prof. Lei Chen Dr. Yangqiu Song **** ALL are Welcome ****