  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.
