156 citations · 361 across the 32 of their papers we have counts for
3 papers · 1 filter
Any Deep ReLU Network is Shallow
Mattia Jacopo Villani, Nandi Schoots
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…
Unwrapping All ReLU Networks
Mattia Jacopo Villani, Peter McBurney
Deep ReLU Networks can be decomposed into a collection of linear models, each defined in a region of a partition of the input space. This paper provides three results extending thi…
Feature Importance for Time Series Data: Improving KernelSHAP
Mattia Villani, Joshua Lockhart, Daniele Magazzeni
Feature importance techniques have enjoyed widespread attention in the explainable AI literature as a means of determining how trained machine learning models make their prediction…