# Deep Neural Decision Trees

arXiv: https://arxiv.org/abs/1806.06988 · HTML: https://mlsanity.com/paper/1806.06988/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2018.

## Built on by (most cited first, 30 of 72)

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- [A Comprehensive Survey on Self-Interpretable Neural Networks](https://mlsanity.com/paper/2501.15638/) (2025)
- [Neural Networks are Decision Trees](https://mlsanity.com/paper/2210.05189/) (2022)
- [Cloud2Curve: Generation and Vectorization of Parametric Sketches](https://mlsanity.com/paper/2103.15536/) (2021)
- [Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent](https://mlsanity.com/paper/2102.07567/) (2021)
- [Learning Interpretable Rules for Scalable Data Representation and Classification](https://mlsanity.com/paper/2310.14336/) (2023)
- [Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations](https://mlsanity.com/paper/2209.13446/) (2022)
- [GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent](https://mlsanity.com/paper/2305.03515/) (2023)
- [GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data](https://mlsanity.com/paper/2309.17130/) (2023)
- [A Survey of Neural Trees](https://mlsanity.com/paper/2209.03415/) (2022)
- [A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era](https://mlsanity.com/paper/2301.09362/) (2023)
- [NCART: Neural Classification and Regression Tree for Tabular Data](https://mlsanity.com/paper/2307.12198/) (2023)
- [Logical Distillation of Graph Neural Networks](https://mlsanity.com/paper/2406.07126/) (2024)
- [DT+GNN: A Fully Explainable Graph Neural Network using Decision Trees](https://mlsanity.com/paper/2205.13234/) (2022)
- [Trade-off Between Efficiency and Consistency for Removal-based Explanations](https://mlsanity.com/paper/2210.17426/) (2022)
- [GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data](https://mlsanity.com/paper/2211.06302/) (2022)
- [Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data](https://mlsanity.com/paper/2211.15616/) (2022)
- [Vanilla Gradient Descent for Oblique Decision Trees](https://mlsanity.com/paper/2408.09135/) (2024)
- [Muddling Label Regularization: Deep Learning for Tabular Datasets](https://mlsanity.com/paper/2106.04462/) (2021)
- [XBNet : An Extremely Boosted Neural Network](https://mlsanity.com/paper/2106.05239/) (2021)

## Abstract (arXiv)

Deep neural networks have been proven powerful at processing perceptual data, such as images and audio. However for tabular data, tree-based models are more popular. A nice property of tree-based models is their natural interpretability. In this work, we present Deep Neural Decision Trees (DNDT) -- tree models realised by neural networks. A DNDT is intrinsically interpretable, as it is a tree. Yet as it is also a neural network (NN), it can be easily implemented in NN toolkits, and trained with gradient descent rather than greedy splitting. We evaluate DNDT on several tabular datasets, verify its efficacy, and investigate similarities and differences between DNDT and vanilla decision trees. Interestingly, DNDT self-prunes at both split and feature-level.