Gamma Belief Network

Gamma belief networks (GBNs) are probabilistic models used to uncover interpretable latent representations in data, particularly focusing on non-negative, sparse, gamma-distributed variables. Current research extends GBNs to more expressive non-linear models and explores their application in diverse fields, including image enhancement (using attention mechanisms and gamma correction), knowledge graph reasoning (leveraging the properties of gamma distributions for improved logical query handling), and recommender systems (incorporating social network information and ordinal variables). These advancements demonstrate the versatility of GBNs and their potential for improving the interpretability and performance of machine learning models across various applications.