The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
TabNet: Attentive Interpretable Tabular Learning
Open accessAbstract
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 variants on a wide range of non-performance-saturated tabular datasets and yields interpretable feature attributions plus insights into its global behavior. Finally, we demonstrate self-supervised learning for tabular data, significantly improving performance when unlabeled data is abundant.
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UCI Machine Learning Repository
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Self-citationTabular data: Deep learning is not all you need
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Review of multimodal machine learning approaches in healthcare
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Beyond Medical Imaging - A Review of Multimodal Deep Learning in Radiology
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Human-in-the-loop tabular data extraction methods for historical climate data rescue
Vercruysse, B., Birkholz, J. M., Chandrasekar, K. K. T., Muheki, D., Thiery, W., Verbeeck, H., Hufkens, K., Jacobsen, K., & Verbruggen, C. (2026) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-025-00524-y · Cited by 5
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Metadata
- Authors
- Arik, S. Ö., & Pfister, T.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2021-05-18
- Volume
- 35
- Issue
- 8
- Pages
- 6679-6687
- ISSN
- 2374-3468
- 2159-5399
- OpenAlex
- W3174086521
- Cited by
- 1,771
- References
- 38
- FWCI
- 61.66
- Type
- journal article
- Language
- en
- Open access
- green
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