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.