Integration of Information Theory and Physics with Data/AI Methods

Collaborative Networked Estimation and Inference through Graph Neural Networks

By Angela Berti13 August 2024NetworksCommunication Networks and Systems, Integration of Information Theory and Physics with Data/AI Methods

Collaborative Networked Estimation and Inference through Graph Neural Networks Graph neural networks (GNNs) are deep learning architectures that learn powerful representations of graphs and graph signals. GNNs have shown remarkable performance across various domains, such as biology and drug discovery, quantum chemistry, robotics, social networks, and recommender systems. Their success can be attributed to their […]

Rethinking Deep Learning Compression Using Information Theoretic Structures

Rethinking Deep Learning Compression Using Information Theoretic Structures Neural compression has brought tremendous progress in designing lossy compressors with good rate-distortion (RD) performance at low complexity. Thus far, neural compression design involves transforming the source to a latent vector, which is then rounded to integers and entropy coded. While this approach has been shown to […]