学术报告

题      目：Efficient and Robust Machine Learning with Tensor Networks

报  告  人：赵启斌  教授  (邀请人：陈艳南 )

                                    RIKEN Center for Advanced Intelligence Project

时      间：11月29日  10：00-11：00

地     点：数科院西楼二楼会议室

报告人简介:

        Qibin Zhao received the Ph.D. degree in computer science from Shanghai Jiao Tong University, China in 2009. He was a research scientist at RIKEN Brain Science Institute from 2009 to 2017.  Since 2017, he has joined RIKEN Center for Advanced Intelligence Project as a unit leader (2017 - 2019) and currently a team leader for tensor learning team. He is also a visiting professor in Tokyo University of Agriculture and Technology and Saitama Institute of Technology, and Guangdong University of Technology. His research interests include machine learning, tensor factorization and tensor networks, computer vision and brain signal processing. He has published more than 150 scientific papers in international journals and conferences and two monographs. He serves as an editorial board member for journal “Science China: Technological Sciences” and Action Editor for “Neural Networks” and “Transaction on Machine Learning Research”, as well as Area Chair for the top-tier ML conference of NeurIPS, ICML, ICLR, AISTATS, AAAI, IJCAI.

摘      要：

        Tensor Networks (TNs) are factorizations of high dimensional tensors into networks of many low-dimensional tensors, which have been studied in quantum physics, high-performance computing, and applied mathematics. In recent years, TNs have been increasingly investigated and applied to machine learning and signal processing, due to its significant advances in handling large-scale and high-dimensional problems, model compression in deep neural networks, and efficient computations for learning algorithms. This talk aims to present some recent progress of TNs technology applied to machine learning from perspectives of basic principle and algorithms, novel approaches in unsupervised learning, tensor completion, multi-model learning and various applications in DNN, CNN, RNN and etc.

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