In the cognitive neurosciences and machine learning, we have formal ways of understanding and characterising perception and decision-making; however, the approaches appear very different: current formulations of perceptual synthesis call on theories like predictive coding and Bayesian brain hypothesis.
While message-passing neural networks (MPNNs) are the most popular architectures for graph learning, their expressive power is inherently limited. In order to gain increased expressive power while retaining efficiency, several recent works apply MPNNs to subgraphs of the original graph.
The IEEE Signal Processing Society (SPS) announces the 2025 Class of Distinguished Lecturers and Distinguished Industry Speakers for the term of 1 January 2025 to 31 December 2026, which are noted below.