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arXiv · 2509.21286

Maxout Polytopes

Andrei Balakin·Shelby Cox·Georg Loho·Bernd Sturmfels
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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.

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BibTeXRIS

Andrei Balakin, Shelby Cox, Georg Loho, Bernd Sturmfels. 2025-09-25. Maxout Polytopes. https://arxiv.org/abs/2509.21286

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