# Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions

arXiv: https://arxiv.org/abs/2501.03540 · HTML: https://mlsanity.com/paper/2501.03540/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2025.

## Built on by (most cited first, 9 of 9)

- [Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges](https://mlsanity.com/paper/2508.00217/) (2025)
- [Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs](https://mlsanity.com/paper/2507.19334/) (2025)
- [Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation](https://mlsanity.com/paper/2510.09671/) (2025)
- [Deep Tabular Research via Continual Experience-Driven Execution](https://mlsanity.com/paper/2603.09151/) (2026)
- [Attention-enabled Explainable AI for Bladder Cancer Recurrence Prediction](https://mlsanity.com/paper/2505.00171/) (2025)
- [Multivariate Uncertainty Quantification with Tomographic Quantile Forests](https://mlsanity.com/paper/2512.16383/) (2025)
- [Innovative tokenisation of structured data for LLM training](https://mlsanity.com/paper/2508.01685/) (2025)
- [UTOPIA: Unlearnable Tabular Data via Decoupled Shortcut Embedding](https://mlsanity.com/paper/2602.07358/) (2026)
- [TabNSM: Neural Sparse Mixer for Tabular Regression](https://mlsanity.com/paper/2608.18026/) (2026)

## Abstract (arXiv)

Tabular data remains one of the most prevalent data types across a wide range of real-world applications, yet effective representation learning for this domain poses unique challenges due to its irregular patterns, heterogeneous feature distributions, and complex inter-column dependencies. This survey provides a comprehensive review of state-of-the-art techniques in tabular data representation learning, structured around three foundational design elements: training data, neural architectures, and learning objectives. Unlike prior surveys that focus primarily on either architecture design or learning strategies, we adopt a holistic perspective that emphasizes the universality and robustness of representation learning methods across diverse downstream tasks. We examine recent advances in data augmentation and generation, specialized neural network architectures tailored to tabular data, and innovative learning objectives that enhance representation quality. Additionally, we highlight the growing influence of self-supervised learning and the adaptation of transformer-based foundation models for tabular data. Our review is based on a systematic literature search using rigorous inclusion criteria, encompassing 127 papers published since 2020 in top-tier conferences and journals. Through detailed analysis and comparison, we identify emerging trends, critical gaps, and promising directions for future research, aiming to guide the development of more generalizable and effective tabular data representation methods.