Neural Network Verification

Neural network verification aims to formally prove that a neural network behaves as intended, ensuring safety and reliability, especially in critical applications. Current research focuses on improving the scalability and efficiency of verification methods, particularly for spiking neural networks and those with general non-linear activation functions, often employing techniques like branch-and-bound, abstract interpretation, and SMT solvers. This field is crucial for building trust in AI systems and enabling their wider adoption in safety-critical domains, such as autonomous vehicles and medical diagnosis, by providing formal guarantees about their behavior.