mlsanityData to 7 Oct 2026

PyTorch Tabular: A Framework for Deep Learning with Tabular Data

arXiv 2104.13638 ↗First posted 28 Apr 2021cs.LGcited by 20

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Abstract (arXiv)

In spite of showing unreasonable effectiveness in modalities like Text and Image, Deep Learning has always lagged Gradient Boosting in tabular data - both in popularity and performance. But recently there have been newer models created specifically for tabular data, which is pushing the performance bar. But popularity is still a challenge because there is no easy, ready-to-use library like Sci-Kit Learn for deep learning. PyTorch Tabular is a new deep learning library which makes working with Deep Learning and tabular data easy and fast. It is a library built on top of PyTorch and PyTorch Lightning and works on pandas dataframes directly. Many SOTA models like NODE and TabNet are already integrated and implemented in the library with a unified API. PyTorch Tabular is designed to be easily extensible for researchers, simple for practitioners, and robust in industrial deployments.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 9 of its 18 references that are in the map. First posted on arXiv 28 Apr 2021.

Builds on

9 of 18 references.

References · 9

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  5. 2019TabNet: Attentive Interpretable Tabular Learningcited by 640
  6. 2018AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networkscited by 319
  7. 2019Neural Oblivious Decision Ensembles for Deep Learning on Tabular Datacited by 200
  8. 2020fastai: A Layered API for Deep Learningcited by 183
  9. 2017Deep Forestcited by 46

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 4; 2024: 7; 2025: 2; 2026: 1.

20 papers.

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  1. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  2. 2022Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Timecited by 85
  3. 2024TALENT: A Tabular Analytics and Learning Toolboxcited by 46
  4. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
  5. 2024A Survey on Deep Tabular Learningcited by 18
  6. 2022Interpretable Mixture of Expertscited by 17
  7. 2025TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Modelscited by 10
  8. 2022GANDALF: Gated Adaptive Network for Deep Automated Learning of Featurescited by 9
  9. 2021Muddling Label Regularization: Deep Learning for Tabular Datasetscited by 5
  10. 2023XMI-ICU: Explainable Machine Learning Model for Pseudo-Dynamic Prediction of Mortality in the ICU for Heart Attack Patientscited by 2
  11. 2023A systematic study comparing hyperparameter optimization engines on tabular datacited by 2
  12. 2024ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainabilitycited by 2
8 more
  1. 2024Fault Detection for agents on power grid topology optimization: A Comprehensive analysiscited by 2
  2. 2022COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Datacited by 1
  3. 2023Bidirectional Attention as a Mixture of Continuous Word Expertscited by 1
  4. 2023CAST: Cluster-Aware Self-Training for Tabular Data via Reliable Confidencecited by 1
  5. 2024TRACE: Transformer-based Risk Assessment for Clinical Evaluationcited by 1
  6. 2026DynaTab: Dynamic Feature Ordering as Neural Rewiring for High-Dimensional Tabular Datacited by 1
  7. 2024MonoSparse-CAM: Efficient Tree Model Processing via Monotonicity and Sparsity in CAMscited by 0
  8. 2024Groningen: Spatial Prediction of Rock Gas Saturation by Leveraging Selected and Augmented Well and Seismic Data with Classifier Ensemblescited by 0