In this work we present a new framework to derive upper bounds on the number regions of feed-forward neural nets with ReLU activation functions. We derive all existing such bounds as special cases, however in a different representation in terms of matrices. This provides new insight and allows a more detailed analysis of the corresponding bounds. In particular, we provide a Jordan-like decomposition for the involved matrices and present new tighter results for an asymptotic setting.
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