Guest Editors' Introduction: Special Section on Learning Deep Architectures
Samy Bengio Li Deng Hugo Larochelle Honglak Lee Ruslan Salakhutdinov
Pages: pp. 1795-1797
About the Authors
Samy Bengio received the PhD degree in computer science from the University of Montreal in 1993. He has been a research scientist at Google since 2007. Before that, he was a senior researcher in statistical machine learning at the IDIAP Research Institute since 1999. His most recent research interests are in machine learning, in particular large scale online learning, learning to rank, image ranking and annotation, music information retrieval, and deep learning. He is action editor of the Journal of Machine Learning Research and on the editorial board of the Machine Learning Journal. He was an associate editor of the Journal of Computational Statistics, general chair of the Workshops on Machine Learning for Multimodal Interactions (MLMI '04, '05, and '06), program chair of the IEEE Workshop on Neural Networks for Signal Processing (NNSP '02), and on the program committee of several international conferences such as NIPS, ICML, and ECML. More information can be found on his website: http://bengio.abracadoudou.com.
Li Deng received the PhD degree in electrical and computer engineering from the University of Wisconsin Madison. He joined the Department of Electrical and Computer Engineering, University of Waterloo, Ontario, Canada, in 1989 as an assistant professor and became a full professor with tenure there in 1996. In 1999, he joined Microsoft Research (MSR), Redmond, Washington, as a Senior Researcher, where he is currently a Principal Researcher. Since 2000, he has also been an affiliate full professor and graduate committee member in the Department of Electrical Engineering at the University of Washington, Seattle. Prior to MSR, he also worked or taught at the Massachusetts Institute of Technology, ATR Interpreting Telecommunications Research Laboratory (Kyoto, Japan), and HKUST. His current research activities include deep learning and machine intelligence, automatic speech and speaker recognition, spoken language understanding, speech-to-speech translation, machine translation, information retrieval, statistical signal processing, and human speech production and perception, and noise robust speech processing. He has been granted more than 60 patents in acoustics/audio, speech/language technology, and machine learning. He is a fellow of the Acoustical Society of America, a fellow of the IEEE, and a fellow of ISCA. He served on the Board of Governors of the IEEE Signal Processing Society (2008-2010). More recently, he served as Editor in Chief for the IEEE Signal Processing Magazine (2009-2011), which ranked first in 2010 and 2011 among all IEEE publications in terms of its impact factor and for which he received the 2011 IEEE SPS Meritorious Service Award. He currently serves as editor-in-chief for the IEEE Transactions on Audio, Speech, and Language Processing.
Hugo Larochelle received the PhD degree in computer science from the University of Montreal in 2009. He is now an assistant professor at the University of Sherbrooke, Canada. He specializes in the development of deep and probabilistic neural networks for high-dimensional and structured data, with a focus on AI-related problems such as natural language processing and computer vision. He is currently an associate editor for the IEEE Pattern Analysis and Machine Intelligence ( TPAMI) and a member of the program committee for the NIPS 2013 conference. He also served on the senior program committee of UAI 2011. He received a Notable Paper Award at the AISTATS 2011 conference and a Google Faculty Research Award.
Honglak Lee received the PhD degree from the Computer Science Department at Stanford University in 2010, advised by Andrew Ng. He is now an assistant professor of computer science and engineering at the University of Michigan, Ann Arbor. His primary research interests lie in machine learning, which spans deep learning, unsupervised and semi-supervised learning, transfer learning, graphical models, and optimization. He also works on application problems in computer vision, audio recognition, and other perception problems. His work received best paper awards at ICML and CEAS. He has coorganized workshops and tutorials related to deep learning at NIPS, ICML, CVPR, and AAAI, and he has served as an area chair for ICML 2013. He received a Google Faculty Research Award and was selected by IEEE Intelligent Systems as one of the AI's 10 to Watch.
Ruslan Salakhutdinov received the PhD degree in computer science from the University of Toronto in 2009. After spending two postdoctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an assistant professor in the Departments of Statistics and Computer Science. His primary interests lie in artificial intelligence, machine learning, deep learning, and large-scale optimization. He is an action editor of the Journal of Machine Learning Research and served on the program committees for several learning conferences, including NIPS, UAI, and ICML. He is an Alfred P. Sloan Research Fellow and Microsoft Research New Faculty Fellow, a recipient of the Early Researcher Award, Connaught New Researcher Award, and is a Scholar of the Canadian Institute for Advanced Research.