Neural denoising diffusion models with non-Gaussian distributions

April 16, 2024 — March 16, 2025

approximation
Bayes
classification
generative
Monte Carlo
neural nets
optimization
probabilistic algorithms
probability
score function
statistics
Figure 1

Placeholder. Diffusion models but for non-Gaussian distributions such a categories.

0.1 Language

See language models for using diffusion models in NLP.

1 References

Austin, Johnson, Ho, et al. 2021. “Structured Denoising Diffusion Models in Discrete State-Spaces.” In Proceedings of the 35th International Conference on Neural Information Processing Systems. NIPS ’21.
Austin, Johnson, Ho, et al. 2023. Structured Denoising Diffusion Models in Discrete State-Spaces.”
Dieleman, Sartran, Roshannai, et al. 2022. Continuous Diffusion for Categorical Data.”
Gruver, Stanton, Frey, et al. n.d. “Protein Design with Guided Discrete Diffusion.”
Han, Zheng, and Zhou. 2022. CARD: Classification and Regression Diffusion Models.”
Hoogeboom, Nielsen, Jaini, et al. 2021. Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions.” In Proceedings of the 35th International Conference on Neural Information Processing Systems. NIPS ’21.
Okhotin, Molchanov, Vladimir, et al. 2023. Star-Shaped Denoising Diffusion Probabilistic Models.” Advances in Neural Information Processing Systems.
Rütte, Fluri, Ding, et al. 2025. Generalized Interpolating Discrete Diffusion.”
Yang, Zhang, Song, et al. 2023. Diffusion Models: A Comprehensive Survey of Methods and Applications.” ACM Computing Surveys.