It's FLAN time! Summing feature-wise latent representations for interpretability

Interpretability has become a necessary feature for machine learning models deployed in critical scenarios, e.g. In these situations, algorithmic decisions may have (potentially negative) long-lasting effects on the end-user affected by the decision. In many cases, the representational power of deep learning models is not needed, therefore simple and interpretable models (e.g. However, in high-dimensional and/or complex domains (e.g. computer vision), the universal approximation capabilities of neural networks is required. Inspired by linear models and the Kolmogorov-Arnol representation theorem, we propose a novel class of structurally-constrained neural networks, which we call FLANs (Feature-wise Latent Additive Networks). These feature-wise latent representations are then simply summed, and the aggregated representation is used for prediction.