Neural denoising diffusion models with non-Gaussian distributions

April 16, 2024 — April 3, 2025

approximation
Bayes
classification
generative
Monte Carlo
neural nets
optimization
probabilistic algorithms
probability
score function
statistics

Diffusion models on non-Euclidean manifolds turn out to be useful. We might imagine they are especially useful for PDEs or non-Gaussian spaces.

I only learned that these models existed about 5 minutes ago, so I won’t claim any depth of insight.

Figure 1

1 References

De Bortoli, Mathieu, Hutchinson, et al. 2022. Riemannian Score-Based Generative Modelling.” Advances in Neural Information Processing Systems.
Jo, and Hwang. 2024. Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes.”
———. 2025. Continuous Diffusion Model for Language Modeling.”
Jo, Kim, and Hwang. 2024. Graph Generation with Diffusion Mixture.”