Stick Breaking Process Mixture

Stick-breaking process mixtures are probabilistic models used to represent complex data distributions as a combination of simpler components, often Gaussian distributions. Current research focuses on improving the efficiency and effectiveness of these models, particularly within Mixture of Experts (MoE) architectures, through techniques like adaptive initialization, progressive expert addition, and the use of hypernetworks for knowledge transfer between experts. These advancements are driving improvements in diverse applications, including image processing (denoising, super-resolution), natural language processing (continual learning in large language models), and structural health monitoring, by enabling more accurate, efficient, and interpretable models.