arXiv Machine Learning By Marie-Charlotte Brandenburg, Moritz Grillo, Christoph Hertrich

Decomposition Polyhedra of Piecewise Linear Functions

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arXiv:2410. 04907v2 Announce Type: replace-cross Abstract: In this paper we contribute to the frequently studied question of how to decompose a continuous piecewise linear (CPWL) function into a difference of two convex CPWL functions.

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arXiv Machine Learning
Sep 18

A Computational Tropical Geometry Framework for Neural Networks

The paper introduces a computational tropical geometry framework for symbolically analyzing neural networks with tropical activations. It presents an algorithm that computes the network’s linear regions as explicit unions of polyhedra, proves its correctness, and connects the number of linear regions to the monomials in the tropical expression. The authors also define the Hoffman constant to bound distances to the farthest linear region and release the open‑source Julia library TropicalNN.jl to implement these tools, demonstrating their use on proof‑of‑concept examples.

By Paul Lezeau, Thomas Walker, Yueqi Cao, Shiv Bhatia, Anthea Monod
arXiv Machine Learning
Jul 27

Shallower ReLU Network Representations via Exact Linear Algebra

arXiv:2607. 21651v1 Announce Type: new Abstract: We prove that the maximum of $n$ real numbers is exactly representable by a ReLU network with two hidden layers for every $n\le 10$.

By Kilian Rue{\ss}, Gennadiy Averkov, Florestan Brunck, Moritz Grillo, Christoph Hertrich, Georg Loho, Jack Stade, Moritz Stargalla, Matthew Sun, Martin Winter