Diffusion Process

Diffusion processes, describing the spread of information or particles over time, are central to various scientific fields and increasingly used in machine learning for generative modeling. Current research focuses on improving the efficiency and accuracy of diffusion models, particularly through advancements in score-matching techniques, novel SDE and ODE solvers, and the development of architectures like JKOnet* for learning diffusion parameters directly from data. These improvements have significant implications for diverse applications, including image generation, medical image analysis (e.g., deepfake detection), and Bayesian inference, by enabling more accurate and efficient estimation and sampling from complex probability distributions.