Deep learning has achieved remarkable success in many machine learning tasks such as
image classification, speech recognition, and game playing. However, these breakthroughs …

Recent years have witnessed growing interest in the application of deep neural networks
(DNNs) for receiver design, which can potentially be applied in complex environments …

Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with
deep neural networks (DNNs) enabling digital receivers to learn how to operate in …

When used in complex engineered systems, such as communication networks, artificial
intelligence (AI) models should be not only as accurate as possible, but also well calibrated …

The aim of this study is to conduct a systematic review of the literature on consumer curiosity
and its impact on consumer behavior. The “Scientific Procedures and Rationales for …

Two of the main principles underlying the life cycle of an artificial intelligence (AI) module in
communication networks are adaptation and monitoring. Adaptation refers to the need to …

In the design of wireless receivers, deep neural networks (DNNs) can be combined with
traditional model-based receiver algorithms to realize modular hybrid model-based/data …

The move toward artificial intelligence (AI)-native sixth-generation (6G) networks has put
more emphasis on the importance of explainability and trustworthiness in network …

Deep learning is envisioned to facilitate the operation of wireless receivers, with emerging
architectures integrating deep neural networks (DNNs) with traditional modular receiver …

Standard Bayesian learning is known to have suboptimal generalization capabilities under
misspecification and in the presence of outliers. Probably approximately correct (PAC) …