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学术讲座通知：Learning representations for semantic relatio

2014/7/10镜像同步0 回复

学术讲座通知讲座题目: Learning representations for semantic relational data主讲人：Patrick Gallinari教授时间：2014年7月11日（周五）下午15:00-17:00地点：教三楼811会议室主持人：高升模式识别实验室内容摘要：Learning representations for complex relational data has emerged at the crossroad between different research topics in machine learning. The motivation of this work is often driven by the applications themselves and by the nature of the data which are often complex (multimodal, heterogeneous, dynamic), and multi-relational (e.g. biology, social networks). One possible approach is to map these data onto one or more continuous latent spaces in order to obtain representations on which it is possible to use classical machine learning methods. In recent years, several lines of research have developed these ideas, sometimes independently, and they are now represented in the “Learning Representations” community. The tools deployed rely on statistical modeling, on linear algebra with matrix or tensor factorization, or more recently on neural networks. The presentation will give a brief presentation of some of these methods and show applications in the field of semantic data analysis and social networks. 此讲座为前沿课题讲座，欢迎全校师生踊跃参加。校学术委员会信息与通信工程学院 2014年7月10日主讲人简介：Patrick. Gallinari is professor in Computer Science at Universite Pierre et Marie Curie (UPMC), France. His research domain is primarily statistical machine learning with applications to domains involving semantic data like information retrieval. His recent work has focused on statistical modeling of complex relational data described by sequences, trees or graphs. Before that, he has been a pioneer of neural networks in France, participating to the development of this domain in Europe. He has also been director of the computer science lab. at UPMC for about 10 years. http://portal.bupt.edu.cn//html/main/col51/2014-07/10/20140710182520159232498_1.html

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