Structured Variational

Structured variational inference is a Bayesian machine learning technique focusing on efficiently approximating complex posterior distributions by imposing structure on the variational family, improving both accuracy and scalability. Current research emphasizes applications in diverse areas, including federated learning, medical image analysis (e.g., disentangling anatomical and pathological features in brain MRIs), graph neural networks (e.g., node feature estimation), and natural language processing (e.g., data-to-text generation). These advancements enable more accurate and efficient inference in large-scale models, leading to improved performance in various applications and facilitating the analysis of previously intractable datasets.