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Advanced Lecture Series in Pattern Recognition - Enhancing Deep Learning with Structures
Jan 12, 2018Author:
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Advanced Lecture Series in Pattern Recognition

TITLE: Enhancing Deep Learning with Structures

SPEAKER: Assoc. Prof. Le Song (Georgia Institute of Technology)

CHAIR: Dr. Yanming Zhang

TIME: 10:30am, January 16 (Tuesday), 2018                        

VENUE: No.1 Conference Room (3rd floor), Intelligence Building


What has made deep learning models so effective? Is it the depth of the models or something else? How can we understand deep learning better and make it even more effective? In this talk, I will argue that the structure of a deep learning model and the landscape of the optimization problem are critically important for the success of such as a model.  I will present both empirical and theoretical evidence for understanding these structure aspects of deep learning, and show that following these findings we can design novel and state-of-the-art deep learning models for a diverse range of applications including face recognition, risk management for Fintech, reasoning over dynamic knowledge graphs, and algorithm design.


Le Song is an Associate Professor in the Department of Computational Science and Engineering, College of Computing, and an Associate Director of the Center for Machine Learning, Georgia Institute of Technology. He received his Ph.D. in Machine Learning under the supervision of Alex Smola from University of Sydney and NICTA in 2008, and then conducted his post-doctoral research with Eric Xing and Carlos Guestrin in the Department of Machine Learning, Carnegie Mellon University, between 2008 and 2011. Before he joined Georgia Institute of Technology in 2011, he was briefly a research scientist with Fernando Pereira at Google. His principal research direction is machine learning, especially kernel and deep embedding methods, and probabilistic graphical models for large scale and complex problems, arising from artificial intelligence, network analysis, computational biology and other interdisciplinary domains. He is the recipient of the NIPS’17 Machine Learning for Molecule sna Materials Workshop Best Paper Award, the Recsys’16 Deep Learning Workshop Best Paper Award, AISTATS'16 Best Student Paper Award, IPDPS'15 Best Paper Award, NSF CAREER Award’14, NIPS’13 Outstanding Paper Award, and ICML’10 Best Paper Award. He has also served as the area chair or senior program committee for many leading machine learning and AI conferences such as ICML, NIPS, AISTATS, AAAI and IJCAI, and the associate editor for JMLR and IEEE TPAMI.