XNNTab -- Interpretable Neural Networks for Tabular Data using Sparse Autoencoders

ArXi:2512.13442v2 Announce Type: replace In data-driven applications relying on tabular data, where interpretability is key, machine learning models such as decision trees and linear regression are applied. Although neural networks can provide higher predictive performance, they are not used because of their blackbox nature. In this work, we present XNNTab, a neural architecture that combines the expressiveness of neural networks and interpretability.