5 papers · 1 filter
Exploring Solver-Level Warmstarting for Neural Network Verification
Annelot Bosman, Minghao Liu, Marta Kwiatkowska +2
Neural network verification has become a key tool for providing formal guarantees on the behaviour of neural networks. However, many verification problems remain computationally in…
Efficient Preimage Approximation for Neural Network Certification
Anton Björklund, Mykola Zaitsev, Paolo Morettin +1
The growing reliance on artificial intelligence in safety- and security-critical applications is raising concerns about the robustness of neural networks to erroneous or adversaria…
Risk-Averse Certification of Bayesian Neural Networks
Xiyue Zhang, Zifan Wang, Yulong Gao +3
In light of the inherently complex and dynamic nature of real-world environments, incorporating risk measures is crucial for the robustness evaluation of deep learning models. In t…
PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks
Xiyue Zhang, Benjie Wang, Marta Kwiatkowska +1
Most methods for neural network verification focus on bounding the image, i.e., set of outputs for a given input set. This can be used to, for example, check the robustness of neur…
Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networks
Matthias König, Xiyue Zhang, Holger H. Hoos +2
The ubiquity of deep learning algorithms in various applications has amplified the need for assuring their robustness against small input perturbations such as those occurring in a…