DANets: Deep Abstract Networks for Tabular Data Classification and Regression. (arXiv:2112.02962v4 [cs.LG] UPDATED)

Tabular data are ubiquitous in real world applications. Although many
commonly-used neural components (e.g., convolution) and extensible neural
networks (e.g., ResNet) have been developed by the machine learning community,
few of them were effective for tabular data and few designs were adequately
tailored for tabular data structures. In this paper, we propose a novel and
flexible neural component for tabular data, called Abstract Layer (AbstLay),
which learns to explicitly group correlative input features and generate
higher-level features for semantics abstraction. …

arxiv classification data data classification networks regression tabular tabular data