Electrical Engineering and Computer Sciences
University of California, Berkeley
Yi Ma is a Professor in residence at the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He received his Bachelor’s degree from Tsinghua University in 1995 and MS and PhD degrees from UC Berkeley in 2000. His research interests are in computer vision, high-dimensional data analysis, and intelligent systems. He has been on the faculty of UIUC ECE from 2000 to 2011, the manager of the Visual Computing group of Microsoft Research Asia from 2009 to 2014, and the Dean of the School of Information Science and Technology of ShanghaiTech University from 2014 to 2017. He has published over 160 papers and three textbooks in computer vision, statistical learning, and data science. He received NSF Career award in 2004 and ONR Young Investigator award in 2005. He also received the David Marr prize in computer vision in 1999 and has served as Program Chair and General Chair of ICCV 2013 and 2015, respectively. He is a Fellow of IEEE, SIAM, and ACM.
Speech Title: Deep Networks from First Principles
Abstract: In this talk, we offer an entirely “white box’’ interpretation of deep (convolution) networks from the perspective of data compression (and group invariance). In particular, we show how modern deep layered architectures, linear (convolution) operators and nonlinear activations, and even all parameters can be derived from the principle of maximizing rate reduction (with group invariance). All layers, operators, and parameters of the network are explicitly constructed via forward propagation, instead of learned via back propagation. All components of so-obtained network, called ReduNet, have precise optimization, geometric, and statistical interpretation. There are also several nice surprises from this principled approach: it reveals a fundamental tradeoff between invariance and sparsity for class separability; it reveals a fundamental connection between deep networks and Fourier transform for group invariance – the computational advantage in the spectral domain (why spiking neurons?); this approach also clarifies the mathematical role of forward propagation (optimization) and backward propagation (variation). In particular, the so-obtained ReduNet is amenable to fine-tuning via both forward and backward (stochastic) propagation, both for optimizing the same objective.
This is joint work with students Yaodong Yu, Ryan Chan, Haozhi Qi of Berkeley, Dr. Chong You now at Google Research, and Professor John Wright of Columbia University.
Microsoft Research Asia, Beijing, China
Wenjun (Kevin) Zeng is a Sr. Principal Research Manager and a member of the Senior Leadership Team (SLT) at Microsoft Research Asia. He is a Fellow of the IEEE. He has been leading the video analytics research powering the Microsoft Cognitive Services, Azure Media Analytics Services, Microsoft Office, Dynamics, and Windows Machine Learning (ML) since 2014. He was with the Computer Science Dept. of Univ. of Missouri (MU) from 2003 to 2016, most recently as a Full Professor. Prior to joining MU in 2003, he worked for PacketVideo Corp, San Diego, CA, Sharp Labs of America, Camas, WA, Bell Labs, Murray Hill, NJ, and Panasonic Technology, Princeton, NJ. He has contributed significantly to the development of international standards (ISO MPEG, JPEG2000, and Open Mobile Alliance). He received his B.E., M.S., and Ph.D. degrees from Tsinghua Univ., the Univ. of Notre Dame, and Princeton Univ., respectively. His current research interest includes mobile-cloud media computing, computer vision, social network/media analysis, and multimedia communications and security.
He is on the Editorial Board of International Journal of Computer Vision. He was an Associate Editor-in-Chief of IEEE Multimedia Magazine, was an Associate Editor (AE) of IEEE Trans. on Circuits & Systems for Video Technology, IEEE Trans. on Info. Forensics & Security, and IEEE Trans. on Multimedia (TMM), and was on the Steering Committee of IEEE Trans. on Mobile Computing (2014-2016) and IEEE TMM (2009-2012). He served as the (first after the 2009 revamp) Steering Committee Chair of IEEE Inter. Conf. Multimedia and Expo (ICME) in 2010 and 2011, and has served as the TPC Chair for several IEEE Conferences (e.g., ICIP’2017, ChinaSIP’2015, WIFS’2013, ICME’2009, CCNC’2007, etc.). He is a General co-Chair of ICME’2018. He was a Guest Editor (GE) of IEEE Communications Magazine Special Issue on Impact of Next-Generation Mobile Technologies on IoT-Cloud Convergence, a GE of TCSVT Special Issue on Video Computing in the Cloud: Mobile Computing, a GE of ACM TOMCCAP Special Issue on ACM MM 2012 Best Papers, a GE of the Proceedings of the IEEE’s Special Issue on Recent Advances in Distributed Multimedia Communications (Jan. 2008) and the Lead GE of IEEE TMM’s Special Issue on Streaming Media (April 2004).
Nanyang Technological University, Singapore
Lin Weisi is an active researcher in intelligent image processing, perception-based signal modelling and assessment, video compression, and multimedia communication. He had been the Lab Head, Visual Processing, in Institute for Infocomm Research (I2R). He is a Professor in School of Computer Science and Engineering, Nanyang Technological University, where he also served as the Associate Chair (Research).
He is a Fellow of IEEE and IET, and has been awarded Highly Cited Researcher 2019 and 2020 by Web of Science. He has elected as a Distinguished Lecturer in both IEEE Circuits and Systems Society (2016-17) and Asia-Pacific Signal and Information Processing Association (2012-13), and given keynote/invited/tutorial/panel talks to 30+ international conferences. He has been an Associate Editor for IEEE Trans. Image Process., IEEE Trans. Circuits Syst. Video Technol., IEEE Trans. Multimedia, IEEE Signal Process. Lett., Quality and User Experience, and J. Visual Commun. Image Represent. He also chaired the IEEE MMTC QoE Interest Group (2012-2014); he has been a TP Chair for IEEE 2013, QoMEX 2014, PV 2015, PCM 2012 and IEEE VCIP 2017. He believes that good theory is practical, and has delivered 10+ major systems and modules for industrial deployment with the technology developed.