[Adapt] FW: 10月14日学术报告：Deep Learning: From Academic Concepts to Industrial Triumph

[Kenny Zhu]

Please go to this talk if you are interested.

 

Kenny 

 


 


Deep Learning: From Academic Concepts to Industrial Triumph


 <http://research.microsoft.com/en-us/people/deng/> Li Deng, Microsoft
Research


october 14, 2013 | 9:30 a.m.
3-200 SEIEE Building


Abstract:

Deep learning is a sub-field of machine learning that focuses on
hierarchical representations of features or concepts, where high-level
semantic-like features can emerge via automatic layer-by-layer learning from
low-level features. In recent years, deep learning has achieved important
successes in a variety of applied artificial intelligence tasks including
speech recognition, computer vision, and natural language processing. The
implications of such recent work have been prominently covered in recent
media (e.g., NYT, Economist, MIT Technology Reviews, Google acquisition of
DNNResearch, etc.). Since 2009, in partnership with leading academic
researchers, Microsoft Research has been pursuing deep learning research and
technology transfer, and has pioneered the development of industry-scale
deep learning technology for speech recognition and other applications,
resulting in industry-wide adoption of deep learning in Windows Phones
(Microsoft), Android Phones (Google), iPhones (Siri of Apple and
Nuance/IBM), and Baidu Phones. In this lecture, I will provide a historical
overview on how academic conceptualization of deep learning rapidly evolved
into wide product deployment worldwide within only a few short years, and
discuss what implications this recent triumphant history may have for future
academic-industrial collaborations. I will also go into some technical depth
in describing the current deep learning technology, and in particular the
disparate approaches which industry and academia take in current pursuits of
the technology. I will conclude by analyzing future directions of deep
learning, and speculating on what types of information processing and
artificial intelligence applications may benefit most from deep learning
technology in light of the known mechanisms of human brain that grounds
intelligence and extreme effectiveness in information processing.

 

Bio:Image removed by sender.

Li Deng received the Ph.D. degree from the University of Wisconsin-Madison.
He was an assistant professor (1989-1992), tenured associate professor
(1992-1996), and Full Professor (1996-1999) at the University of Waterloo,
Ontario, Canada. In 1999, he joined Microsoft Research, Redmond, WA, where
he is currently a Principal Researcher. Prior to MSR, he also worked or
taught at Massachusetts Institute of Technology, ATR Interpreting Telecom.
Research Lab. (Kyoto, Japan), and HKUST. He has been granted over 60 US or
international patents in acoustics/audio, speech/language technology, and
machine learning. He received numerous awards/honors bestowed by IEEE, ISCA,
ASA, and Microsoft. In the general areas of audio/speech/language technology
and science, machine learning, and signal/information processing, he has
published over 300 refereed papers in leading journals and conferences and 4
books. He is a Fellow of the Acoustical Society of America (ASA), a Fellow
of the IEEE, and a Fellow of the International Speech Communication
Association (ISCA). He served on the Board of Governors of the IEEE Signal
Processing Society (2008-2010), and as Editor-in-Chief for the IEEE Signal
Processing Magazine (2009-2011). He serves as a General Chair of the IEEE
ICASSP-2013, and as Editor-in-Chief for the IEEE/ACM Transactions on Audio,
Speech and Language Processing. He initiated the deep learning work within
Microsoft in 2009 (working with Prof. Geoff Hinton “in house”), with its
inspiration and influence soon spread to the industry. His technical work
and the leadership since 2009 in industry-scale deep learning with
colleagues and academic collaborators have created significant impact in
speech recognition and the related areas of information processing including
information retrieval, spoken language understanding, speech synthesis,
image recognition, machine translation, and web search.

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