# XBNet : An Extremely Boosted Neural Network

arXiv: https://arxiv.org/abs/2106.05239 · HTML: https://mlsanity.com/paper/2106.05239/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 9 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Deep Neural Decision Trees](https://mlsanity.com/paper/1806.06988/) (2018)

## Built on by (most cited first, 5 of 5)

- [Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning](https://mlsanity.com/paper/2406.13846/) (2024)
- [RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization](https://mlsanity.com/paper/2311.03115/) (2023)
- [Clinical Deterioration Prediction in Brazilian Hospitals Based on Artificial Neural Networks and Tree Decision Models](https://mlsanity.com/paper/2212.08975/) (2022)
- [Dimensionality Reduction Using pseudo-Boolean polynomials For Cluster Analysis](https://mlsanity.com/paper/2308.15553/) (2023)
- [Closing the gap on tabular data with Fourier and Implicit Categorical Features](https://mlsanity.com/paper/2602.23182/) (2026)

## Abstract (arXiv)

Neural networks have proved to be very robust at processing unstructured data like images, text, videos, and audio. However, it has been observed that their performance is not up to the mark in tabular data; hence tree-based models are preferred in such scenarios. A popular model for tabular data is boosted trees, a highly efficacious and extensively used machine learning method, and it also provides good interpretability compared to neural networks. In this paper, we describe a novel architecture XBNet, which tries to combine tree-based models with that of neural networks to create a robust architecture trained by using a novel optimization technique, Boosted Gradient Descent for Tabular Data which increases its interpretability and performance.