Neural diffusion models



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Suggestive connection to thermodynamics (Sohl-Dickstein et al. 2015).

References

Dhariwal, Prafulla, and Alex Nichol. 2021. β€œDiffusion Models Beat GANs on Image Synthesis.” arXiv:2105.05233 [Cs, Stat], June.
Ho, Jonathan, Ajay Jain, and Pieter Abbeel. 2020. β€œDenoising Diffusion Probabilistic Models.” arXiv:2006.11239 [Cs, Stat], December.
Hoogeboom, Emiel, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, and Tim Salimans. 2021. β€œAutoregressive Diffusion Models.” arXiv:2110.02037 [Cs, Stat], October.
Nichol, Alex, and Prafulla Dhariwal. 2021. β€œImproved Denoising Diffusion Probabilistic Models.” arXiv:2102.09672 [Cs, Stat], February.
Sohl-Dickstein, Jascha, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. β€œDeep Unsupervised Learning Using Nonequilibrium Thermodynamics.” arXiv:1503.03585 [Cond-Mat, q-Bio, Stat], November.
Song, Jiaming, Chenlin Meng, and Stefano Ermon. 2021. β€œDenoising Diffusion Implicit Models.” arXiv:2010.02502 [Cs], November.
Song, Yang, and Stefano Ermon. 2020a. β€œGenerative Modeling by Estimating Gradients of the Data Distribution.” arXiv:1907.05600 [Cs, Stat], October.
β€”β€”β€”. 2020b. β€œImproved Techniques for Training Score-Based Generative Models.” arXiv:2006.09011 [Cs, Stat], October.

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