Bound propagation methods, when combined with branch and bound, are among the most
effective methods to formally verify properties of deep neural networks such as correctness …

Bound propagation based incomplete neural network verifiers such as CROWN are very
efficient and can significantly accelerate branch-and-bound (BaB) based complete …

Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …

Certified robustness is a desirable property for deep neural networks in safety-critical
applications, and popular training algorithms can certify robustness of a neural network by …

This manuscript presents the updated version of the Neural Network Verification (NNV) tool.
NNV is a formal verification software tool for deep learning models and cyber-physical …

We present a novel approach to efficiently compute tight non-convex enclosures of the
image through neural networks with ReLU, sigmoid, or hyperbolic tangent activation …

Inspired by sum-of-infeasibilities methods in convex optimization, we propose a novel
procedure for analyzing verification queries on neural networks with piecewise-linear …

We are interested in predicting failures of cyber-physical systems during their operation.
Particularly, we consider stochastic systems and signal temporal logic specifications, and we …

Fully formal verification of perception models is likely to remain challenging in the
foreseeable future, and yet these models are being integrated into safety-critical control …

We introduce a novel notion of perception contracts to reason about the safety of controllers
that interact with an environment using neural perception. Perception contracts capture …