mlsanityData to 7 Oct 2026

Deep Neural Decision Trees

arXiv 1806.06988 ↗First posted 19 Jun 2018cs.LG stat.MLcited by 72

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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.

Key facts. Cited by 72 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jun 2018.

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Citing papers per year: 2021: 22; 2022: 15; 2023: 12; 2024: 12; 2025: 6; 2026: 5.

72 papers.

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  1. 2021Revisiting Deep Learning Models for Tabular Datacited by 725
  2. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 364
  3. 2021A Survey on Interpretable Reinforcement Learningcited by 70
  4. 2022Transfer Learning with Deep Tabular Modelscited by 52
  5. 2021Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restorationcited by 50
  6. 2021DANets: Deep Abstract Networks for Tabular Data Classification and Regressioncited by 50
  7. 2021Boost then Convolve: Gradient Boosting Meets Graph Neural Networkscited by 42
  8. 2022T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interactioncited by 41
  9. 2021Scalable Rule-Based Representation Learning for Interpretable Classificationcited by 40
  10. 2022ExSpliNet: An interpretable and expressive spline-based neural networkcited by 33
  11. 2021Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Errorcited by 28
  12. 2025A Comprehensive Survey on Self-Interpretable Neural Networkscited by 18
38 more
  1. 2021Cloud2Curve: Generation and Vectorization of Parametric Sketchescited by 16
  2. 2022Neural Networks are Decision Treescited by 16
  3. 2021Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descentcited by 14
  4. 2023Learning Interpretable Rules for Scalable Data Representation and Classificationcited by 14
  5. 2022Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanationscited by 11
  6. 2023GradTree: Learning Axis-Aligned Decision Trees with Gradient Descentcited by 11
  7. 2023GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Datacited by 10
  8. 2022A Survey of Neural Treescited by 7
  9. 2023A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Eracited by 7
  10. 2023NCART: Neural Classification and Regression Tree for Tabular Datacited by 7
  11. 2022DT+GNN: A Fully Explainable Graph Neural Network using Decision Treescited by 6
  12. 2022Trade-off Between Efficiency and Consistency for Removal-based Explanationscited by 6
  13. 2022GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Datacited by 6
  14. 2022Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Datacited by 6
  15. 2024Logical Distillation of Graph Neural Networkscited by 6
  16. 2024Vanilla Gradient Descent for Oblique Decision Treescited by 6
  17. 2021Muddling Label Regularization: Deep Learning for Tabular Datasetscited by 5
  18. 2021XBNet : An Extremely Boosted Neural Networkcited by 5
  19. 2024Learning Exceptional Subgroups by End-to-End Maximizing KL-divergencecited by 5
  20. 2022Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guaranteescited by 4
  21. 2022Mixed integer linear optimization formulations for learning optimal binary classification treescited by 4
  22. 2024Kernel KMeans clustering splits for end-to-end unsupervised decision treescited by 4
  23. 2021Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semanticscited by 3
  24. 2023Memorization Capacity of Neural Networks with Conditional Computationcited by 3
  25. 2024A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networkscited by 3
  26. 2024Practical Attribution Guidance for Rashomon Setscited by 3
  27. 2024Language Model as Visual Explainercited by 3
  28. 2021Yet Another Representation of Binary Decision Trees: A Mathematical Demonstrationcited by 2
  29. 2022MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learningcited by 2
  30. 2023A Gradient Boosting Approach for Training Convolutional and Deep Neural Networkscited by 2
  31. 2023Enabling tabular deep learning when d gg n with an auxiliary knowledge graphcited by 2
  32. 2024Diffusion Boosted Treescited by 2
  33. 2025Learning Interpretable Differentiable Logic Networks for Tabular Regressioncited by 2
  34. 2021Efficient Click-Through Rate Prediction for Developing Countries via Tabular Learningcited by 1
  35. 2021Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-Tuningcited by 1
  36. 2021NEWRON: A New Generalization of the Artificial Neuron to Enhance the Interpretability of Neural Networkscited by 1
  37. 2021Learning To Segment Dominant Object Motion From Watching Videoscited by 1
  38. 2022Transition Matrix Representation of Trees with Transposed Convolutionscited by 1

50 most cited of 72 All 72 (JSON)