2:00PM - Maury 104
Abstract: 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 however apparently been limited, which makes it difficult to be fully developed. In this talk, we study the capacity as well as generalization properties of deep neural networks (DNN) under different scenarios of weight normalization. We also discuss how to use DNN for nonlinear variable selection. If time permits, we provide an understanding of deep learning from an automated modeling perspective. This understanding leads to a sequential method of constructing deep learning models.