From Generalized Linear Models to Neural Networks, and Back

From Generalized Linear Models to Neural Networks, and Back

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Description:

We discuss the statistical modeling cycle. This discussion highlights on how to enhance classical generalized linear models by neural network features. On the way to get there, we mention traps and pitfalls that need to be avoided to get good statistical models. This includes the non-uniqueness of "sufficiently good" regression models, the balance property, and representation learning, which brings us back to the concepts of the good old generalized linear models.

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