讲座题目: Learning representations for semantic relational data
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.
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.