May 14, 2024, 4:44 a.m. | Danny D'Agostino, Ilija Ilievski, Christine Annette Shoemaker

cs.LG updates on

arXiv:2307.05639v2 Announce Type: replace
Abstract: Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives. To address this challenge, we propose a modification of the radial basis function neural network model by equipping its Gaussian kernel with a learnable precision matrix. We show that precious information is contained in the spectrum of the precision matrix …

arxiv cs.lg features functions networks neural networks replace type

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