Neural Galerkin Normalizing Flow for Transition Probability Density Functions of Diffusion Models
arXiv:2603.18907v2 Announce Type: replace Abstract: We propose a new Neural Galerkin Normalizing Flow framework to approximate the transition probability density function of a diffusion process by solving the corresponding Fokker-Planck equation with an atomic initial distribution, parametrically with respect to the location of the initial mass. By using Normalizing Flows, we look for the solution as a transformation of the transition probability density function of a reference stochastic process, ensuring that our approximation is structure-preserving and automatically satisfies positivity an
This development appears at a time of intense research into improving the efficiency and accuracy of diffusion models, which are central to generative AI progress.
A more efficient and accurate method for modeling transition probability density functions could significantly advance the capabilities of generative AI, particularly in areas requiring precise control over outputs.
The ability to accurately model the temporal evolution of diffusion processes with neural networks could lead to more robust and controllable AI systems.
- · AI researchers
- · Generative AI developers
- · Machine learning hardware providers
- · Less efficient diffusion model methodologies
Improved performance and broader applications of diffusion models.
Accelerated development of AI agents capable of more complex and nuanced interactions.
Potentially, new paradigms for understanding and simulating physical or biological systems currently modeled by diffusion processes.
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Read at arXiv cs.LG