  In this paper we present ExSpliNet, an interpretable and expressive neural
network model. The model combines ideas of Kolmogorov neural networks,
ensembles of probabilistic trees, and multivariate B-spline representations. We
give a probabilistic interpretation of the model and show its universal
approximation properties. We also discuss how it can be efficiently encoded by
exploiting B-spline properties. Finally, we test the effectiveness of the
proposed model on synthetic approximation problems and classical machine
learning benchmark datasets.
