Showing 2023Show all
2 papers · 1 filter
cs.LG2023
1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness
Bernd Prach, Fabio Brau, Giorgio Buttazzo +1
The robustness of neural networks against input perturbations with bounded magnitude represents a serious concern in the deployment of deep learning models in safety-critical syste…
cs.LG2023
1-Lipschitz Neural Networks are more expressive with N-Activations
Bernd Prach, Christoph H. Lampert
A crucial property for achieving secure, trustworthy and interpretable deep learning systems is their robustness: small changes to a system's inputs should not result in large chan…