KEYNOTE: LocalGLMnet: An Interpretable Deep Learning Architecture
We present a new deep learning architecture called LocalGLMnet. While deep learning models lead to very competitive regression models, often outperforming classical statistical models such as generalized linear models, the disadvantage is that deep learning solutions are difficult to interpret and explain, and variable selection is not easily possible. Inspired by the appealing structure of generalized linear models, we propose a new network architecture that shares similar features as generalized linear models, but provides superior predictive power benefiting from the art of representation learning. This new architecture allows one for variable selection of tabular data and for interpretation of the calibrated deep learning model.