Simple Modifications to Improve Tabular Neural Networks

Abstract: There is growing interest in neural network architectures for tabular data. Many general-purpose tabular deep learning models have been introduced recently, with performance sometimes rivaling gradient boosted decision trees (GBDTs). These recent models draw inspiration from various sources, including GBDTs, factorization machines, and neural networks from other application domains. Previous tabular neural networks are also drawn upon, but are possibly under-considered, especially models associated with specific tabular problems. This paper focuses on several such models, and proposes modifications for improving their performance. When modified, these models are shown to be competitive with leading general-purpose tabular models, including GBDTs.