Research into Explainable Artificial Intelligence (XAI) has been increasing in recent years as
a response to the need for increased transparency and trust in AI. This is particularly …

Deep reinforcement learning (DRL) has proven capable of superhuman performance on
many complex tasks. To achieve this success, DRL algorithms train a decision-making agent …

Deep neural networks are revolutionizing the way complex systems are designed.
Consequently, there is a pressing need for tools and techniques for network analysis and …

Deep neural networks are increasingly being used as controllers for safety-critical systems.
Because neural networks are opaque, certifying their correctness is a significant challenge …

As deep reinforcement learning (RL) showcases its strengths in networking, its pitfalls are
also coming to the public's attention. Training on a wide range of network environments …

The Open Radio Access Network (RAN) paradigm is transforming cellular networks into a
system of disaggregated, virtualized, and software-based components. These self-optimize …

While many deep learning (DL)-based networking systems have demonstrated superior
performance, the underlying Deep Neural Networks (DNNs) remain blackboxes and stay …

The application of deep reinforcement learning (DRL) to computer and networked systems
has recently gained significant popularity. However, the obscurity of decisions by DRL …

Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …

Deep neural networks (DNNs) are revolutionizing the way complex systems are designed,
developed and maintained. As part of the life cycle of DNN-based systems, there is often a …