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arXiv:2306.11827cs.LG

Any Deep ReLU Network is Shallow

Mattia Jacopo Villani, Nandi Schoots

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Abstract

We constructively prove that every deep ReLU network can be rewritten as a functionally identical three-layer network with weights valued in the extended reals. Based on this proof, we provide an algorithm that, given a deep ReLU network, finds the explicit weights of the corresponding shallow network. The resulting shallow network is transparent and used to generate explanations of the model s behaviour.

arXiv comments
12 pages including bibliography and appendix
Journal reference
DOI
Subjects
cs.LGMachine Learning cs.AIArtificial Intelligence stat.MLMachine Learning (Statistics)

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