[1] Mohammadreza Armandpour, Huangjie Zheng, Ali Sadeghian, Amir Sadeghian, and Mingyuan Zhou. Re-imagine the negative prompt algorithm: Transform 2D diffusion into 3D, alleviate Janus problem and beyond. arXiv preprint arXiv:2304.04968, 2023. URL https://perp-neg.github.io/. (the first three authors contributed equally).
[2] Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg. Structured denoising diffusion models in discrete state-spaces. Advances in Neural Information Processing Systems, 34:17981-17993, 2021.
[3] Pavel Avdeyev, Chenlai Shi, Yuhao Tan, Kseniia Dudnyk, and Jian Zhou. Dirichlet diffusion score model for biological sequence generation. arXiv preprint arXiv:2305.10699, 2023.
[4] Arindam Banerjee, Srujana Merugu, Inderjit S Dhillon, Joydeep Ghosh, and John Lafferty. Clustering with Bregman divergences. Journal of machine learning research, 6(10), 2005.
[5] Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Cold diffusion: Inverting arbitrary image transforms without noise. arXiv preprint arXiv:2208.09392, 2022.
[6] O. Barndorff-Nielsen. Information and Exponential Families : In Statistical Theory. Wiley Series in Probability and Statistics Ser. John Wiley & Sons, Incorporated, New York, 2nd ed. edition, 2014. ISBN 9781118857373.
[7] David M Blei, Alp Kucukelbir, and Jon D McAuliffe. Variational inference: A review for statisticians. Journal of the American statistical Association, 112(518):859-877, 2017.
[8] Andrew Campbell, Joe Benton, Valentin De Bortoli, Tom Rainforth, George Deligiannidis, and Arnaud Doucet. A continuous time framework for discrete denoising models. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. URL https://openreview.net/forum?id=DmT862YAieY.
[9] Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan. WaveGrad: Estimating gradients for waveform generation. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=NsMLjcFa080.
[10] Tianqi Chen and Mingyuan Zhou. Learning to Jump: Thinning and thickening latent counts for generative modeling. In ICML 2023: International Conference on Machine Learning, July 2023. URL http://arxiv.org/abs/2305.18375.
[11] Thomas M Cover and Joy A Thomas. Elements of information theory. 1991.
[12] Giannis Daras, Maurizio Delbracio, Hossein Talebi, Alex Dimakis, and Peyman Milanfar. Soft diffusion: Score matching with general corruptions. Transactions on Machine Learning Research, 2023. ISSN 2835-8856. URL https://openreview.net/forum?id=W98rebXlQ.
[13] Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, and Arnaud Doucet. Riemannian score-based generative modelling. Advances in Neural Information Processing Systems, 35:2406-2422, 2022.
[14] Prafulla Dhariwal and Alexander Quinn Nichol. Diffusion models beat GANs on image synthesis. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021. URL https://openreview.net/forum?id=AAWuCvzaVt.