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Subject-specific Deep Neural Networks for Count Data with High-cardinality Categorical Features
In this paper, we propose a novel hierarchical likelihood learning framework for introducing gamma random effects into the Poisson DNN, so as to improve the prediction performance by capturing both nonlinear effects of input variables and subject-specific cluster effects.
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