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ExSpliNet: An interpretable and expressive spline-based neural network. (arXiv:2205.01510v1 [cs.LG])
May 4, 2022, 1:11 a.m. | Daniele Fakhoury, Emanuele Fakhoury, Hendrik Speleers
cs.LG updates on arXiv.org arxiv.org
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.
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