Deep neural networks (DNNs) are increasingly being used as controllers in reactive
systems. However, DNNs are highly opaque, which renders it difficult to explain and justify …

In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in
machine learning due to its successful applications to real-world and complex systems …

Cost functions are commonly employed in Safe Deep Reinforcement Learning (DRL).
However, the cost is typically encoded as an indicator function due to the difficulty of …

Safety is essential for deploying Deep Reinforcement Learning (DRL) algorithms in real-
world scenarios. Recently, verification approaches have been proposed to allow quantifying …

Identifying safe areas is a key point to guarantee trust for systems that are based on Deep
Neural Networks (DNNs). To this end, we introduce the AllDNN-Verification problem: given a …

In recent years, Machine Learning (ML) models have achieved remarkable success in
various domains. However, these models also tend to demonstrate unsafe behaviors …

The field of robotic Flexible Endoscopes (FEs) has progressed significantly, offering a
promising solution to reduce patient discomfort. However, the limited autonomy of most …

Deep Neural Networks (DNNs) are powerful tools that have shown extraordinary results in
many scenarios, ranging from pattern recognition to complex robotic problems. However …

Deep Neural Networks (DNN) are crucial in approximating nonlinear functions across
diverse applications, ranging from image classification to control. Verifying specific input …

Probabilistic verification of neural networks is concerned with formally analysing the output
distribution of a neural network under a probability distribution of the inputs. Examples of …