3 papers
cs.CC2026
Parameterized Complexity of -Lipschitz Constants for Input Convex Neural Networks and -Norm Maximization over Zonotopes
Aritra Das, Vincent Froese, Moritz Grillo +6
Lipschitz constants are a standard way to quantify the sensitivity of neural networks to small input perturbations, but computing them is difficult even for shallow ReLU networks.…
cs.CC2026
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.CC2025
Complexity of Injectivity and Verification of ReLU Neural Networks
Vincent Froese, Moritz Grillo, Martin Skutella
Neural networks with ReLU activation play a key role in modern machine learning. Understanding the functions represented by ReLU networks is a major topic in current research as th…