Over the last decade, smart cities (SC) have been developed worldwide. Implementing big
data and the internet of things improves the monitoring and integration of different …

Spiking neural networks (SNNs) have garnered increased attention in the field of artificial
general intelligence (AGI) research due to their low power consumption, high computational …

We review the current literature concerned with information plane (IP) analyses of neural
network (NN) classifiers. While the underlying information bottleneck theory and the claim …

Active inference offers a first principle account of sentient behavior, from which special and
important cases—for example, reinforcement learning, active learning, Bayes optimal …

Information bottleneck (IB) is a technique for extracting information in one random variable X
that is relevant for predicting another random variable Y. IB works by encoding X in a …

This survey is for the remembrance of one of the creators of the information bottleneck
theory, Prof. Naftali Tishby, passing away at the age of 68 on August, 2021. Information …

Medical image segmentation plays an important role in disease diagnosis and surgical
guidance. There are two problems in the current field of medical image segmentation. First …

Federated Learning (FL) is an emerging technique for collaboratively training machine
learning models on distributed data under privacy constraints. However, recent studies have …

Background Accurate medical image segmentation is crucial for disease diagnosis and
surgical planning. Transformer networks offer a promising alternative for medical image …

In recent years, semantic communication has received significant attention from both
academia and industry, driven by the growing demands for ultra-low latency and high …