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IEEE Signal Processing Washington Chapter 2013 Distinguished Lecture
Thursday, October 3, 2013
5:00 p.m.
1107 Kim Building (Kay Boardroom)
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IEEE Signal Processing Washington Chapter 2013 Distinguished Lecture
Deep Learning: From Academic Concept to Industrial Triumph

Dr. Li Deng
Principal Researcher

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.

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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