In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …

The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …

Large language models (LLMs) have exploded a new heatwave of AI for their ability to
engage end-users in human-level conversations with detailed and articulate answers across …

Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

Despite the improved accuracy of deep neural networks, the discovery of adversarial
examples has raised serious safety concerns. In this paper, we study two variants of …

Abstract Medical applications of Artificial Intelligence (AI) have consistently shown
remarkable performance in providing medical professionals and patients with support for …

Deep neural networks (DNNs) have been shown lack of robustness, as they are vulnerable
to small perturbations on the inputs, which has led to safety concerns on applying DNNs to …

Abstract We present VeriX (Verified eXplainability), a system for producing optimal robust
explanations and generating counterfactuals along decision boundaries of machine …

The advent of sophisticated robotics and AI technology makes sending humans into
hazardous and distant environments to carry out inspections increasingly avoidable. Being …

Neural network controllers (NNCs) have shown great promise in autonomous and cyber-
physical systems. Despite the various verification approaches for neural networks, the safety …