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cs.LG2026
Shallower ReLU Network Representations via Exact Linear Algebra
Kilian RueÃ, Gennadiy Averkov, Florestan Brunck +7
We prove that the maximum of real numbers is exactly representable by a ReLU network with two hidden layers for every . The constructions are obtained by reducing the…
cs.LG2026
Better Neural Network Expressivity: Subdividing the Simplex
Egor Bakaev, Florestan Brunck, Christoph Hertrich +2
This work studies the expressivity of ReLU neural networks with a focus on their depth. A sequence of previous works showed that hidden layers are suffi…
cs.LG2025
On the Depth of Monotone ReLU Neural Networks and ICNNs
Egor Bakaev, Florestan Brunck, Christoph Hertrich +2
We study two models of ReLU neural networks: monotone networks (ReLU) and input convex neural networks (ICNN). Our focus is on expressivity, mostly in terms of depth, and we pr…