# Multiple Additive Neural Networks for Structured and Unstructured Data

arXiv: https://arxiv.org/abs/2604.26888 · HTML: https://mlsanity.com/paper/2604.26888/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 57 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 21 of 21)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [Early stopping and non-parametric regression: An optimal data-dependent stopping rule](https://mlsanity.com/paper/1306.3574/) (2013)
- [Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study](https://mlsanity.com/paper/2002.00772/) (2020)
- [Adaptive Neural Trees](https://mlsanity.com/paper/1807.06699/) (2018)
- [DeepCaps: Going Deeper with Capsule Networks](https://mlsanity.com/paper/1904.09546/) (2019)
- [Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learning](https://mlsanity.com/paper/1802.05800/) (2018)
- [VideoCapsuleNet: A Simplified Network for Action Detection](https://mlsanity.com/paper/1805.08162/) (2018)
- [Efficient Saliency Maps for Explainable AI](https://mlsanity.com/paper/1911.11293/) (2019)
- [Capsule Network Performance on Complex Data](https://mlsanity.com/paper/1712.03480/) (2017)
- [Brain Tumor Type Classification via Capsule Networks](https://mlsanity.com/paper/1802.10200/) (2018)
- [Capsule Networks for Low Resource Spoken Language Understanding](https://mlsanity.com/paper/1805.02922/) (2018)
- [Fast CapsNet for Lung Cancer Screening](https://mlsanity.com/paper/1806.07416/) (2018)
- [Sequential Training of Neural Networks with Gradient Boosting](https://mlsanity.com/paper/1909.12098/) (2019)
- [Convolutional capsule network for classification of breast cancer histology images](https://mlsanity.com/paper/1804.08376/) (2018)
- [Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks](https://mlsanity.com/paper/1805.04424/) (2018)
- [SelfieBoost: A Boosting Algorithm for Deep Learning](https://mlsanity.com/paper/1411.3436/) (2014)
- [A Comparative Study for Predicting Heart Diseases Using Data Mining Classification Methods](https://mlsanity.com/paper/1704.02799/) (2017)

## Abstract (arXiv)

This paper extends and explains the Multiple Additive Neural Networks (MANN) methodology, an enhancement to the traditional Gradient Boosting framework, utilizing nearly shallow neural networks instead of decision trees as base learners. This innovative approach leverages neural network architectures, notably Convolutional Neural Networks (CNNs) and Capsule Neural Networks, to extend its application to both structured data and unstructured data such as images and audio. For structured data the advantages of capsule neural networks as feature extractors are used and combined with MANN as a classifier. MANN's unique architecture promotes continuous learning and integrates advanced heuristics to combat overfitting, ensuring robustness and reducing sensitivity to hyperparameter settings like learning rate and iterations. Our empirical studies reveal that MANN surpasses traditional methods such as Extreme Gradient Boosting (XGB) in accuracy across well-known datasets. This research demonstrates MANN's superior precision and generalizability, making it a versatile tool for diverse data types and complex learning environments.