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TabNet: Attentive Interpretable Tabular Learning

arXiv 1908.07442 ↗First posted 20 Aug 2019cs.LG stat.MLcited by 640

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Abstract (arXiv)

We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet. TabNet uses sequential attention to choose which features to reason from at each decision step, enabling interpretability and more efficient learning as the learning capacity is used for the most salient features. We demonstrate that TabNet outperforms other neural network and decision tree variants on a wide range of non-performance-saturated tabular datasets and yields interpretable feature attributions plus insights into the global model behavior. Finally, for the first time to our knowledge, we demonstrate self-supervised learning for tabular data, significantly improving performance with unsupervised representation learning when unlabeled data is abundant.

Key facts. Cited by 640 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Aug 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 45; 2022: 72; 2023: 125; 2024: 117; 2025: 152; 2026: 129.

640 papers.

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  1. 2021Revisiting Deep Learning Models for Tabular Datacited by 725
  2. 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 643
  3. 2021Tabular Data: Deep Learning is Not All You Needcited by 530
  4. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  5. 2021SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Trainingcited by 310
  6. 2022Language Models are Realistic Tabular Data Generatorscited by 290
  7. 2022TabLLM: Few-shot Classification of Tabular Data with Large Language Modelscited by 273
  8. 2023When Do Neural Nets Outperform Boosted Trees on Tabular Data?cited by 218
  9. 2022Why do tree-based models still outperform deep learning on tabular data?cited by 198
  10. 2022On Embeddings for Numerical Features in Tabular Deep Learningcited by 185
  11. 2021FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured informationcited by 166
  12. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
38 more
  1. 2021Well-tuned Simple Nets Excel on Tabular Datasetscited by 151
  2. 2022LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Taskscited by 146
  3. 2022TransTab: Learning Transferable Tabular Transformers Across Tablescited by 132
  4. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 123
  5. 2021Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learningcited by 112
  6. 2024TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensemblingcited by 105
  7. 2021SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learningcited by 101
  8. 2021NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learningcited by 88
  9. 2022Reliability of CKA as a Similarity Measure in Deep Learningcited by 82
  10. 2024Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learningcited by 79
  11. 2023To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Reviewcited by 72
  12. 2024CARTE: Pretraining and Transfer for Tabular Learningcited by 69
  13. 2023Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Datacited by 68
  14. 2024Retrieval & Fine-Tuning for In-Context Tabular Modelscited by 58
  15. 2024Making Pre-trained Language Models Great on Tabular Predictioncited by 56
  16. 2023Generative Table Pre-training Empowers Models for Tabular Predictioncited by 55
  17. 2023Relational Deep Learning: Graph Representation Learning on Relational Databasescited by 55
  18. 2022Transfer Learning with Deep Tabular Modelscited by 52
  19. 2022OpenFE: Automated Feature Generation with Expert-level Performancecited by 52
  20. 2021DANets: Deep Abstract Networks for Tabular Data Classification and Regressioncited by 50
  21. 2025PyG 2.0: Scalable Learning on Real World Graphscited by 49
  22. 2024TuneTables: Context Optimization for Scalable Prior-Data Fitted Networkscited by 47
  23. 2024TALENT: A Tabular Analytics and Learning Toolboxcited by 46
  24. 2024Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extractioncited by 44
  25. 2022Revisiting Pretraining Objectives for Tabular Deep Learningcited by 43
  26. 2021Boost then Convolve: Gradient Boosting Meets Graph Neural Networkscited by 42
  27. 2022Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Taskscited by 42
  28. 2022T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interactioncited by 41
  29. 2025Griffin: Towards a Graph-Centric Relational Database Foundation Modelcited by 41
  30. 2025A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilitiescited by 38
  31. 2024SwitchTab: Switched Autoencoders Are Effective Tabular Learnerscited by 37
  32. 2023Trompt: Towards a Better Deep Neural Network for Tabular Datacited by 36
  33. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
  34. 2023HYTREL: Hypergraph-enhanced Tabular Data Representation Learningcited by 35
  35. 2021Locally Sparse Neural Networks for Tabular Biomedical Datacited by 34
  36. 2022On Grounded Planning for Embodied Tasks with Language Modelscited by 33
  37. 2023ExcelFormer: A neural network surpassing GBDTs on tabular datacited by 33
  38. 2025TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problemscited by 33

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