MANN combines gradient boosting with neural networks instead of trees, enabling a single framework to handle structured and unstructured data while outperforming XGBoost and reducing hyperparameter sensitivity.
This paper presents Multiple Additive Neural Networks (MANN), which replaces decision trees in gradient boosting with shallow neural networks. MANN works with both structured data and images/audio by using CNNs and capsule networks as feature extractors, and shows better accuracy than XGBoost on standard benchmarks while being more robust to hyperparameter choices.