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cs.LG2025
On the expressivity of sparse maxout networks
Moritz Grillo, Tobias Hofmann
We study the expressivity of sparse maxout networks, where each neuron takes a fixed number of inputs from the previous layer and employs a, possibly multi-argument, maxout activat…
cs.CC2025
Parameterized Hardness of Zonotope Containment and Neural Network Verification
Vincent Froese, Moritz Grillo, Christoph Hertrich +1
Neural networks with ReLU activations are a widely used model in machine learning. It is thus important to have a profound understanding of the properties of the functions computed…
cs.LG2025
Depth-Bounds for Neural Networks via the Braid Arrangement
Moritz Grillo, Christoph Hertrich, Georg Loho
We contribute towards resolving the open question of how many hidden layers are required in ReLU networks for exactly representing all continuous and piecewise linear functions on…