Deep Neural Networks and Tabular Data: A Survey

Heterogeneous tabular data are the most commonly used form of data and are essential for numerous critical and computationally demanding applications. Our results, which we have made publicly available as competitive benchmarks, indicate that algorithms based on gradient-boosted tree ensembles still mostly outperform deep learning models on supervised learning tasks, suggesting that the research progress on competitive deep learning models for tabular data is stagnating.

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• 1 On homogeneous datasets, deep neural networks have repeatedly shown excellent performance and have therefore been widely adopted.
• 2 However, their adaptation to tabular data for inference or data generation tasks remains highly challenging.
• 3 To facilitate further progress in the field, this work provides an overview of advanced deep learning methods for tabular data.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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