Unification of Deep Learning and Reasoning
Dapeng Oliver Wu
Professor of Electrical & Computer Engineering University of Florida, USA.
Abstract: While deep learning has achieved a huge success in various learning problems, the current models are still far away from replicating many functions that a normal human brain can do. Memorization based deep architecture have been recently proposed with the objective to learn and predict better. In this talk, I will present a model that involves a primary learner with an adjacent structured memory bank which can not only predict the output from a given input but also relate it to all its past memorized instances and help in its creative understanding. This paper presents a spatially forked deep learning architecture that can even predict and reason about the nature of an input belonging to a category never seen in the training data by relating it with the memorized past representations at the higher layers. Characterizing images of unseen geometrical figures is used as an example to showcase the operational success of the proposed framework.
Biography: Dapeng Oliver Wu received Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, PA, in 2003. Since 2003, he has been on the faculty of Electrical and Computer Engineering Department at University of Florida, Gainesville, FL, where he is currently Professor. His research interests are in the areas of networking, communications, video coding, image processing, computer vision, signal processing, and machine learning.
He received University of Florida Term Professorship Award in 2017, University of Florida Research Foundation Professorship Award in 2009, AFOSR Young Investigator Program (YIP) Award in 2009, ONR Young Investigator Program (YIP) Award in 2008, NSF CAREER award in 2007, the IEEE Circuits and Systems for Video Technology (CSVT) Transactions Best Paper Award for Year 2001, the Best Paper Award in GLOBECOM 2011, and the Best Paper Award in QShine 2006. Currently, he serves as Editor-in-Chief of IEEE Transactions on Network Science and Engineering, and Associate Editor of IEEE Transactions on Communications, IEEE Transactions on Signal and Information Processing over Networks, and IEEE Signal Processing Magazine. He was the founding Editor-in-Chief of Journal of Advances in Multimedia between 2006 and 2008, and an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Wireless Communications and IEEE Transactions on Vehicular Technology. He has served as Technical Program Committee (TPC) Chair for IEEE INFOCOM 2012. He was elected as a Distinguished Lecturer by IEEE Vehicular Technology Society in 2016. He is an IEEE Fellow.
Machine Learning for Cybersecurity and Smart Nation Applications
School of Information Systems Singapore Management University (SMU) Singapore
Abstract: In this talk, I will first give an overview of our research projects in the context of cybersecurity and smart nation applications and introduce the opportunities and challenges of machine learning research. I will then present some example projects in cybersecurity and smart nation (such as malicious ULR detection, healthy diet and smart food consumption), and demonstrate how the state-of-the-art deep learning technique can be applied to tackle the practical challenges in the real-world projects. I will then discuss the limitations of traditional machine learning and deep learning methodologies, and finally highlight some frontier research topics of emerging machine learning beyond traditional deep learning approaches.
Biography: Dr. Steven Hoi is currently Associate Professor in the School of Information Systems (SIS), Singapore Management University (SMU). Prior to joining SMU, he was a tenured Associate Professor at the School of Computer Engineering of Nanyang Technological University (NTU), Singapore. He received his Bachelor degree from Tsinghua University, and his Master and Ph.D degrees from the Chinese University of Hong Kong. His research interests include machine learning (online learning, deep learning, and beyond) with application to big data analytics across various real-world applications, including multimedia retrieval, computer vision and pattern recognition, cybersecurity, web search and information retrieval, social media analytics, computational finance, mobile and software mining, healthcare, etc. He has published 200+ papers in top conferences and premier journals, and served as an organizer, area chair, senior PC, TPC member, editor and referee for many top conferences and premier journals. He was the recipients of the Lee Kuan Yew Fellowship and the Lee Kong Chian Fellowship awards for Research Excellence. He is the Editor-in-Chief of Neurocomputing journal.