  A challenging open question in deep learning is how to handle tabular data.
Unlike domains such as image and natural language processing, where deep
architectures prevail, there is still no widely accepted neural architecture
that dominates tabular data. As a step toward bridging this gap, we present
DNF-Net a novel generic architecture whose inductive bias elicits models whose
structure corresponds to logical Boolean formulas in disjunctive normal form
(DNF) over affine soft-threshold decision terms. In addition, DNF-Net promotes
localized decisions that are taken over small subsets of the features. We
present an extensive empirical study showing that DNF-Nets significantly and
consistently outperform FCNs over tabular data. With relatively few
hyperparameters, DNF-Nets open the door to practical end-to-end handling of
tabular data using neural networks. We present ablation studies, which justify
the design choices of DNF-Net including the three inductive bias elements,
namely, Boolean formulation, locality, and feature selection.
