The discrete diffusion NELBO equals data entropy plus an exact path KL to the oracle reverse process, and the denoiser, cavity, and score parameterizations are three interconvertible coordinates of the unique optimal reverse jump rate.
Advances in Neural Information Processing Systems , volume =
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Diffusion, score-based, and flow matching models are unified as instances of learning time-dependent vector fields inducing marginal distributions governed by continuity and Fokker-Planck equations.
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What Does a Discrete Diffusion Model Learn?
The discrete diffusion NELBO equals data entropy plus an exact path KL to the oracle reverse process, and the denoiser, cavity, and score parameterizations are three interconvertible coordinates of the unique optimal reverse jump rate.
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A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models
Diffusion, score-based, and flow matching models are unified as instances of learning time-dependent vector fields inducing marginal distributions governed by continuity and Fokker-Planck equations.