Neural Diffusion

Neural diffusion models are a rapidly evolving class of generative models that leverage the principles of diffusion processes to learn complex data distributions, offering advantages over traditional methods like GANs. Current research focuses on extending these models to various data types, including images, point clouds, graphs, and even program syntax trees, often incorporating neural differential equations and graph neural networks within novel architectures like diffusion-augmented neural processes and neural flow diffusion models. This approach shows promise for diverse applications, from high-resolution image synthesis and 3D modeling to robust point cloud registration and improved inference in scientific fields like astroparticle physics and climate modeling.