K. Sundmacher

Optimization with Region-Reduced ReLU Neural Networks

This work proposes a novel \emph{region-reduced} model compression approach that combines the stabilization of unstable neurons and the merging of redundant neurons to reduce the number of linear regions while maintaining predictive accuracy through error compensation.