# PyTorch Tabular: A Framework for Deep Learning with Tabular Data

arXiv: https://arxiv.org/abs/2104.13638 · HTML: https://mlsanity.com/paper/2104.13638/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data](https://mlsanity.com/paper/1909.06312/) (2019)
- [fastai: A Layered API for Deep Learning](https://mlsanity.com/paper/2002.04688/) (2020)
- [Deep Forest](https://mlsanity.com/paper/1702.08835/) (2017)

## Built on by (most cited first, 21 of 21)

- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time](https://mlsanity.com/paper/2211.14238/) (2022)
- [TALENT: A Tabular Analytics and Learning Toolbox](https://mlsanity.com/paper/2407.04057/) (2024)
- [Representation Learning for Tabular Data: A Comprehensive Survey](https://mlsanity.com/paper/2504.16109/) (2025)
- [A Survey on Deep Tabular Learning](https://mlsanity.com/paper/2410.12034/) (2024)
- [Interpretable Mixture of Experts](https://mlsanity.com/paper/2206.02107/) (2022)
- [TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models](https://mlsanity.com/paper/2511.02802/) (2025)
- [GANDALF: Gated Adaptive Network for Deep Automated Learning of Features](https://mlsanity.com/paper/2207.08548/) (2022)
- [Muddling Label Regularization: Deep Learning for Tabular Datasets](https://mlsanity.com/paper/2106.04462/) (2021)
- [XMI-ICU: Explainable Machine Learning Model for Pseudo-Dynamic Prediction of Mortality in the ICU for Heart Attack Patients](https://mlsanity.com/paper/2305.06109/) (2023)
- [A systematic study comparing hyperparameter optimization engines on tabular data](https://mlsanity.com/paper/2311.15854/) (2023)
- [ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability](https://mlsanity.com/paper/2406.09046/) (2024)
- [Fault Detection for agents on power grid topology optimization: A Comprehensive analysis](https://mlsanity.com/paper/2406.16426/) (2024)
- [TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks](https://mlsanity.com/paper/2606.02384/) (2026)
- [COVID-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 Data](https://mlsanity.com/paper/2204.11210/) (2022)
- [Bidirectional Attention as a Mixture of Continuous Word Experts](https://mlsanity.com/paper/2307.04057/) (2023)
- [CAST: Cluster-Aware Self-Training for Tabular Data via Reliable Confidence](https://mlsanity.com/paper/2310.06380/) (2023)
- [TRACE: Transformer-based Risk Assessment for Clinical Evaluation](https://mlsanity.com/paper/2411.08701/) (2024)
- [DynaTab: Dynamic Feature Ordering as Neural Rewiring for High-Dimensional Tabular Data](https://mlsanity.com/paper/2605.03430/) (2026)
- [MonoSparse-CAM: Efficient Tree Model Processing via Monotonicity and Sparsity in CAMs](https://mlsanity.com/paper/2407.11071/) (2024)
- [Groningen: Spatial Prediction of Rock Gas Saturation by Leveraging Selected and Augmented Well and Seismic Data with Classifier Ensembles](https://mlsanity.com/paper/2410.10371/) (2024)

## 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.