IEEE CIS Distinguished Lecturer Program

The State of the Art of Multiple-winners-take-all Networks: Neurodynamics formulation, Models, and Applications

Prof. Jun Wang
Department of Mechanical & Automation Engineering
The Chinese University of Hong Kong

DEI - Seminar Room
May 14th, 2012
10 a.m.


Winner-take-all is a general rule commonly used in many applications such as machine learning and data mining. K-winners-take-all is a generalization of winner-take-all with multiple winners. Over the last twenty years, many K-winners-take-all neural networks and circuits have been developed with varied complexity and performance. In this talk, I will start with several mathematical problem formulations of the K-winners-take-all solutions via neurodynamic optimization, then present several K winners-take-all networks with reducing model complexity based on our neurodynamic optimization models. Finally, we will introduce the best one with the simplest model complexity and maximum computational efficiency. Analytical and Monte Carlo simulation results will be shown to demonstrate the computing characteristics and performance. The applications to parallel sorting, rank-order filtering, and information retrieval will be also discussed.

Short bio
Jun Wang is a Professor and the Director of the Computational Intelligence Laboratory in the Department of Mechanical and Automation Engineering at the Chinese University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, and University of North Dakota. He also held various short-term visiting positions at USAF Armstrong Laboratory (1995), RIKEN Brain Science Institute (2001), Universite Catholique de Louvain (2001), Chinese Academy of Sciences (2002), Huazhong University of Science and Technology (2006–2007), and Shanghai Jiao Tong University (2008-2011) as a Changjiang Chair Professor. Since 2011, he is a National Thousand-Talent Chair Professor at Dalian University of Technology on a part-time basis. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published 150 journal papers, 13 book chapters, 8 edited books, and numerous conference papers in these areas. He has been an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics – Part B since 2003 and a member of the Editorial Advisory Board of the International Journal of Neural Systems since 2006 and of the Editorial Board of the Neural Networks since 2012. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009) and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), and Neurocomputing (2008). He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence. He served as the President of the Asia Pacific Neural Network Assembly in 2006 and various IEEE committees (e.g., Fellow Committee). He is an IEEE Fellow, an IEEE Distinguished Lecturer, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award and APNNA Outstanding Achievement Award in 2011.

Cesare Alippi

Research area:
Systems Architectures