Machine learning has evolved into an enabling technology for a wide range of highly
successful applications. The potential for this success to continue and accelerate has placed …

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 …

We study the verification problem for closed-loop dynamical systems with neural-network
controllers (NNCS). This problem is commonly reduced to computing the set of reachable …

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 …

Systematic testing of autonomous vehicles operating in complex real-world scenarios is a
difficult and expensive problem. We present Paracosm, a framework for writing systematic …

Control Barrier Functions (CBF) have been recently utilized in the design of provably safe
feedback control laws for nonlinear systems. These feedback control methods typically …

Classification is one of the interesting areas in the academic field of Neural Networks.
Artificial Neural Networks (ANNs) have been extensively used in pattern recognition and …

The use of learning based components in cyber-physical systems (CPS) has created a
gamut of possible avenues to use high dimensional real world signals generated from …

Editor's notes: Neural network control systems are often at the heart of autonomous systems.
The authors classify existing verification methods for these systems and advocate the …

Verification of deep neural networks has witnessed a recent surge of interest, fueled by
success stories in diverse domains and by abreast concerns about safety and security in …