Methods › General › Deep Tabular Learning › TabNet
TabNet
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
TabNet is a deep tabular data learning architecture that uses sequential attention to choose which features to reason from at each decision step.
The TabNet encoder is composed of a feature transformer, an attentive transformer and feature masking. A split block divides the processed representation to be used by the attentive transformer of the subsequent step as well as for the overall output. For each step, the feature selection mask provides interpretable information about the model’s functionality, and the masks can be aggregated to obtain global feature important attribution. The TabNet decoder is composed of a feature transformer block at each step.
In the feature transformer block, a 4-layer network is used, where 2 are shared across all decision steps and 2 are decision step-dependent. Each layer is composed of a fully-connected (FC) layer, BN and GLU nonlinearity. An attentive transformer block example – a single layer mapping is modulated with a prior scale information which aggregates how much each feature has been used before the current decision step. sparsemax is used for normalization of the coefficients, resulting in sparse selection of the salient features.
Papers archive 2025-07-28
29 shown of 29, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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The Impact of Feature Scaling In Machine Learning: Effects on Regression and Classification Tasks 9 Jun 2025 · 0 repositories · arXiv:2506.08274
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DuAL-Net: A Hybrid Framework for Alzheimer's Disease Prediction from Whole-Genome Sequencing via Local SNP Windows and Global Annotations 31 May 2025 · 0 repositories · arXiv:2506.00673
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Fusion of Foundation and Vision Transformer Model Features for Dermatoscopic Image Classification 22 May 2025 · 0 repositories · arXiv:2505.16338
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Benchmarking Traditional Machine Learning and Deep Learning Models for Fault Detection in Power Transformers 7 May 2025 · 1 repository · arXiv:2505.06295
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Can Moran Eigenvectors Improve Machine Learning of Spatial Data? Insights from Synthetic Data Validation 16 Apr 2025 · 0 repositories · arXiv:2504.12450
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Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals 9 Apr 2025 · 1 repository · arXiv:2504.06987
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A Survey on Deep Tabular Learning 15 Oct 2024 · 0 repositories · arXiv:2410.12034
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Enhanced Credit Score Prediction Using Ensemble Deep Learning Model 30 Sep 2024 · 0 repositories · arXiv:2410.00256
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Gradient Boosting Decision Trees on Medical Diagnosis over Tabular Data 25 Sep 2024 · 1 repository · arXiv:2410.03705
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Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes 31 Aug 2024 · 0 repositories · arXiv:2409.00485
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Interpretable Graph Neural Networks for Heterogeneous Tabular Data 14 Aug 2024 · 2 repositories · arXiv:2408.07661Syntology ran 4 of 5 samples · 1 unverified
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InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation 1 Jun 2024 · 1 repository · arXiv:2406.00426Syntology ran 10 of 15 samples · 5 unverified
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Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank? 13 May 2024 · 1 repository · arXiv:2405.07782
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Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case 3 May 2024 · 0 repositories · arXiv:2405.02060
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TabVFL: Improving Latent Representation in Vertical Federated Learning 27 Apr 2024 · 0 repositories · arXiv:2404.17990
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FH-TabNet: Multi-Class Familial Hypercholesterolemia Detection via a Multi-Stage Tabular Deep Learning 16 Mar 2024 · 0 repositories · arXiv:2403.11032
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Exploring Factors Affecting Pedestrian Crash Severity Using TabNet: A Deep Learning Approach 29 Nov 2023 · 1 repository · arXiv:2312.00066
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Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data 18 Nov 2023 · 0 repositories · arXiv:2311.10962
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Stable and Interpretable Deep Learning for Tabular Data: Introducing InterpreTabNet with the Novel InterpreStability Metric 4 Oct 2023 · 0 repositories · arXiv:2310.02870
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Interpretable Graph Neural Networks for Tabular Data 17 Aug 2023 · 2 repositories · arXiv:2308.08945
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Machine learning methods for the search for L&T brown dwarfs in the data of modern sky surveys 6 Aug 2023 · 1 repository · arXiv:2308.03045
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Pump It Up: Predict Water Pump Status using Attentive Tabular Learning 8 Apr 2023 · 0 repositories · arXiv:2304.03969
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Tiny Classifier Circuits: Evolving Accelerators for Tabular Data 28 Feb 2023 · 0 repositories · arXiv:2303.00031
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Interpreting Black-box Machine Learning Models for High Dimensional Datasets 29 Aug 2022 · 0 repositories · arXiv:2208.13405
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Don't read, just look: Main content extraction from web pages using visual features 27 Oct 2021 · 0 repositories · arXiv:2110.14164
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Analysis of Vision-based Abnormal Red Blood Cell Classification 1 Jun 2021 · 0 repositories · arXiv:2106.00389
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PyTorch Tabular: A Framework for Deep Learning with Tabular Data 28 Apr 2021 · 1 repository · arXiv:2104.13638
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Fairness in TabNet Model by Disentangled Representation for the Prediction of Hospital No-Show 6 Mar 2021 · 0 repositories · arXiv:2103.04048
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TabNet: Attentive Interpretable Tabular Learning 20 Aug 2019 · 19 repositories · arXiv:1908.07442Syntology ran 1 of 17 samples · 16 unverified · 1 pointer-only (licence)
Tasks archive 2025-07-28
20 shown of 49 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections