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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 ****