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.

翻译：最近引入了许多通用表格深层学习模型,其性能有时与梯度推动的决策树相对应。这些最新模型从各种来源,包括GBDT、集成机和其他应用领域的神经网络中得到启发。以前的表格神经网络也得到利用,但可能考虑不足,特别是与具体表格问题有关的模型。本文件侧重于若干这类模型,并提出改进这些模型的改进建议。经过修改后,这些模型与主要通用表格模型,包括GBDT具有竞争力。