Papers › A Continuous Time Framework for Discrete Denoising Models
A Continuous Time Framework for Discrete Denoising Models
Andrew Campbell, Joe Benton, Valentin De Bortoli, Tom Rainforth, George Deligiannidis, Arnaud Doucet
We provide the first complete continuous time framework for denoising diffusion models of discrete data. This is achieved by formulating the forward noising process and corresponding reverse time generative process as Continuous Time Markov Chains (CTMCs). The model can be efficiently trained using a continuous time version of the ELBO. We simulate the high dimensional CTMC using techniques developed in chemical physics and exploit our continuous time framework to derive high performance samplers that we show can outperform discrete time methods for discrete data. The continuous time treatment also enables us to derive a novel theoretical result bounding the error between the generated sample distribution and the true data distribution.
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Syntology Ran 9 of 12 code samples harvested from 2 repositories linked to this paper; 3 have no recorded run. Of those that ran: 2 ran · our draft was wrong; 5 ran · fixture could not drive it; 2 ran with no contract checked.
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Code Syntology ran Syntology
12 samples harvested; 9 ran; 0 honoured the contract we drafted; 3 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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