Learning Representations from Data

Learning Representations from Data

时间：2013年10月27日 地点：九龙湖校区计算机学院四楼会议室

报告简介：

As the availability and scope of complex data increase in both scientific and engineering fields, developing statistical tools to discover latent structures and hidden explanatory factors has become a major theme of statistic and machine learning research. Breakthrough work has recently been done on learning latent feature representations with shallow or deep architectures using a huge amount of computing resources and massive data corpora. However, some key research issues are still remaining under-addressed. In this talk, I will introduce some of our recent work on learning representations that are discriminative in specific application tasks, including classification, multi-view data analysis, social link prediction, and low-rank matrix factorization. I will share some insights on dealing with several key issues on learning latent representations, including discriminative ability, model complexity, sparsity/interpretability, and scalability.

报告人简介：

朱军，清华大学计算机科学与技术系副教授、博士生导师，中国计算机学会优秀论文奖、微软学者、以及国家优秀青年基金获得者，入选清华大学221基础研究人才计划。2009到2011年在美国卡内基梅隆大学机器学习系做博士后研究。主要从事机器学习、贝叶斯统计等基础理论、算法及相关应用研究。相关工作在国际期刊与会议JMLR, PAMI, ICML, NIPS等发表论文40余篇。受邀担任机器学习顶级国际会议ICML2014的联合地区主席、ICML2014和NIPS2013的领域主席、 IJCAI2013的资深程序委员等。2013年入选IEEE Intelligent Systems国际杂志评选的 "AI's 10 to Watch"。

计算机网络和信息集成教育部重点实验室（东南大学）