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Learning Representations from Data

時間：2013年10月27日 地點：九龍湖校區計算機學院四樓會議室

報告簡介：

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.

報告人簡介：

朱軍，清華大學計算機科學與技術系副教授、博士生導師，中國計算機學會優秀論文獎、微軟學者、以及國家優秀青年基金獲得者，入選清華大學221基礎研究人才計劃。2009到2011年在美國卡内基梅隆大學機器學習系做博士後研究。主要從事機器學習、貝葉斯統計等基礎理論、算法及相關應用研究。相關工作在國際期刊與會議JMLR, PAMI, ICML, NIPS等發表論文40餘篇。受邀擔任機器學習頂級國際會議ICML2014的聯合地區主席、ICML2014和NIPS2013的領域主席、 IJCAI2013的資深程序委員等。2013年入選IEEE Intelligent Systems國際雜志評選的 "AI's 10 to Watch"。

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