Maxout Polytopes

Andrei Balakin, Shelby Cox, Georg Loho, Bernd Sturmfels

Abstract: Maxout polytopes are defined by feedforward neural networks with maxout activation function and non-negative weights after the first layer. We characterize the parameter spaces and extremal f-vectors of maxout polytopes for shallow networks, and we study the separating hypersurfaces which arise when a layer is added to the network. We also show that maxout polytopes are cubical for generic networks without bottlenecks.

External IDs:dblp:journals/corr/abs-2509-21286