DMTK: Making Very Large Scale Machine Learning Possible

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"The Beauty of Artificial Intelligence Seminar Series"

Date:           Monday, 25 April 2016
Time:           10:00am - 12 noon
Venue:          Lecture Theater G (near lifts 25/26), HKUST

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(Seminar II)
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Speaker:        Taifeng WANG
                Lead Researcher
                Microsoft Research Asia

Title:          "DMTK: Making Very Large Scale Machine Learning Possible"

Time:           10:30am to 11:10am

Abstract:

Distributed machine learning has become more important than ever in this
big data era. Especially in recent years, practices have demonstrated the
trend that bigger models tend to generate better accuracies in various
applications. However, it remains a challenge for common machine learning
researchers and practitioners to learn big models, because the task
usually requires a large number of computation resources. In order to
enable the training of big models using just a modest cluster and in an
efficient manner, we released the Microsoft Distributed Machine Learning
Toolkit (DMTK), which contains both algorithmic and system innovations.
These innovations make machine learning tasks on big data highly scalable,
efficient and flexible.

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Biography:

Taifeng Wang is now a lead researcher in Artificial Intelligence group,
Microsoft Research Asia. He joined MSRA in July 2006 after graduating from
University of Science and Technology of China. His research interest
includes large scale machine learning, computational advertising and
distributed system. He is currently leading a project in MSRA focusing on
building a parallel machine learning platform. Prior to that, he had been
working on sponsored ads and search engine techniques for several years,
primarily working on ads click prediction, ads keyword selection, ads
optimization and search engine static ranking algorithm. He has published
papers and served as PC on premium conferences such as WWW, KDD, AAAI,
WSDM, and SIGIR. In addition, he has shipped several techniques to Windows
Azure machine learning and Bing ads based on his research works. He also
has over 10 related US patents filed or under processing.