Call for Nominations: Editor-in-Chief IEEE Transactions on Artificial Intelligence
The IEEE Computational Intelligence Society; IEEE Computer Society; IEEE Systems, Man, and Cybernetics Society; IEEE Signal Processing Society; and…
Read moreDeep Learning on Graphs: History, Successes, Challenges, and Next Steps
Deep learning on graphs, also known as Geometric deep learning (GDL) [1], Graph representation learning (GRL), or relational inductive biases, has recently become one of the hottest topics in machine learning. While early works on graph learning go back at least a decade [2], if not two [3], it is undoubtedly the past few years’ progress that has taken these methods from a niche into the spotlight of the Machine Learning (ML) community.
Artificial Intelligence in Radio Frequencies
Artificial intelligence (AI) and machine learning (ML) as an application of AI, has today become an inevitable part of major industries such as healthcare, financial trending, and transportation. Future urgent need to intelligently utilize wireless resources to meet the need of ever-increasing diversity in services and user behavior, has actuated the wireless communication industry to deploy AI and ML techniques.