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  • 统计与管理学院2017年学术报告第34

     

    【主  题】Automated Model Building and Deep Learning

    【报告人】王啸, 教授

    Purdue University

    【时  间】 2017年05月24日(星期三)15:00-16:00

    【地  点】 上海财经大学统计与管理学院大楼1208室

    【摘  要】Analysis of big data demands computer aided or even automated model building. It becomes extremely difficult to analyze such data with traditional statistical models and model building methods. Deep learning has proved to be successful for a variety of challenging problems such as AlphaGo, driverless cars, and image classification. Understanding deep learning has apparently limited, which makes it difficult to be fully developed. In this talk, we focus on neural network models with one hidden layers. We provide an understanding of deep learning from an automated modeling perspective. This understanding leads to a sequential method of constructing deep learning models. This method is also adaptive to unknown underlying model structure. This is a joint work with Chuanghai Liu.

    【邀请人】 冯兴东