# DNF-Net: A Neural Architecture for Tabular Data

arXiv: https://arxiv.org/abs/2006.06465 · HTML: https://mlsanity.com/paper/2006.06465/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 17 of 17)

- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [TransTab: Learning Transferable Tabular Transformers Across Tables](https://mlsanity.com/paper/2205.09328/) (2022)
- [CARTE: Pretraining and Transfer for Tabular Learning](https://mlsanity.com/paper/2402.16785/) (2024)
- [DANets: Deep Abstract Networks for Tabular Data Classification and Regression](https://mlsanity.com/paper/2112.02962/) (2021)
- [PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning](https://mlsanity.com/paper/2404.00776/) (2024)
- [BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model](https://mlsanity.com/paper/2404.03830/) (2024)
- [Hopular: Modern Hopfield Networks for Tabular Data](https://mlsanity.com/paper/2206.00664/) (2022)
- [A Survey on Deep Tabular Learning](https://mlsanity.com/paper/2410.12034/) (2024)
- [Simple Modifications to Improve Tabular Neural Networks](https://mlsanity.com/paper/2108.03214/) (2021)
- [GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data](https://mlsanity.com/paper/2309.17130/) (2023)
- [TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets](https://mlsanity.com/paper/2204.07615/) (2022)
- [The GatedTabTransformer. An enhanced deep learning architecture for tabular modeling](https://mlsanity.com/paper/2201.00199/) (2022)
- [A Gradient Boosting Approach for Training Convolutional and Deep Neural Networks](https://mlsanity.com/paper/2302.11327/) (2023)
- [Neural Network-Based Rule Models With Truth Tables](https://mlsanity.com/paper/2309.09638/) (2023)
- [ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold](https://mlsanity.com/paper/2604.13392/) (2026)
- [A New Interpretable Neural Network-Based Rule Model for Healthcare Decision Making](https://mlsanity.com/paper/2309.11101/) (2023)

## Abstract (arXiv)

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.