Deep learning has emerged as an effective approach for creating modern software systems,
with neural networks often surpassing hand-crafted systems. Unfortunately, neural networks …

Deep learning methods can be used to produce control policies, but certifying their safety is
challenging. The resulting networks are nonlinear and often very large. In response to this …

A central quest in explainable AI relates to understanding the decisions made by (learned)
classifiers. There are three dimensions of this understanding that have been receiving …

Concerned with the reliability of neural networks, researchers have developed verification
techniques to prove their robustness. Most verifiers work with real-valued networks …

In this paper, we investigate the computational intelligibility of Boolean classifiers,
characterized by their ability to answer XAI queries in polynomial time. The classifiers under …

Deep learning has become a promising programming paradigm in software development,
owing to its surprising performance in solving many challenging tasks. Deep neural …

Verifying and explaining the behavior of neural networks is becoming increasingly
important, especially when they are deployed in safety-critical applications. In this paper, we …

Efforts to apply transformer-based language models (TLMs) to the problem of reasoning in
natural language have enjoyed ever-increasing success in recent years. The most …

Quantized deep neural networks (DNNs) have the potential to find wide applications in
safety-critical cyber–physical systems implemented on processors supporting only integer …

Explainable Artificial Intelligence (XAI) is widely regarding as a cornerstone of trustworthy AI.
Unfortunately, most work on XAI offers no guarantees of rigor. In high-stakes domains, eg …