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A Continuous Time Framework for Discrete Denoising Models

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arxiv 2205.14987 v2 pith:PNRB4QY5 submitted 2022-05-30 stat.ML cs.LG

Andrew Campbell , Joe Benton , Valentin De Bortoli , Tom Rainforth , George Deligiannidis , Arnaud Doucet This is my paper · ORCID
classification stat.MLcs.LG
keywords timecontinuousdiscretedataframeworkdenoisingderivedistribution
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 7 citations worldwide. Full citation record

  1. CANDI: Hybrid Discrete-Continuous Diffusion Models

    cs.LG 2025-10 conditional novelty 6.0 of 10

    CANDI combines masked and Gaussian corruption in one noising process, letting discrete diffusion models use continuous gradients for joint updates and guidance.

  2. Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo

    cs.LG 2025-02 conditional novelty 6.0 of 10

    An SMC importance-sampling algorithm debiases discrete diffusion guidance, asymptotically sampling from the target tempered distribution p0(x0)p(ζ|x0)^α.

  3. Discrete State Diffusion Models: A Sample Complexity Perspective

    cs.LG 2025-10 reject novelty 5.0 of 10

    Claims the first Õ(ε⁻²) sample-complexity bound for discrete-state diffusion, but the zero-approximation-error, optimization-error, and hardness lemmas carrying the proof are internally broken.

  4. Masked Diffusion Language Models with Frequency-Informed Training

    cs.CL 2025-09 conditional novelty 4.0 of 10

    Masked diffusion language models trained on 100M words match a hybrid GPT-BERT baseline on BabyLM tests, with a rare-word-focused masking variant.

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