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Particle Denoising Diffusion Sampler

Denoising diffusion models have become ubiquitous for generative modeling. The core idea is to transport the data distribution to a Gaussian by using a diffusion. Approximate samples from the data distribution are then obtained by estimating the time-reversal of this diffusion using score matching ideas.

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Cite this as

Angus Phillips, Hai-Dang Dau, Michael John Hutchinson, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet (2024). Dataset: Particle Denoising Diffusion Sampler. https://doi.org/10.57702/08y2pl95

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Additional Info

Field	Value
Created	December 2, 2024
Last update	December 2, 2024
Defined In	https://doi.org/10.48550/arXiv.2402.06320
Author	Angus Phillips
More Authors	Hai-Dang Dau Michael John Hutchinson Valentin De Bortoli George Deligiannidis Arnaud Doucet