Mean-to-Score Discrete Diffusion: Posterior-Mean Denoisers for Score Entropy
Score Entropy Discrete Diffusion (SEDD) parameterizes discrete reverse processes with unconstrained positive score ratios. While positivity guarantees nonnegative reverse jump rates, it does not ensure Bayes realizability: ratios at a noisy state need not be jointly induced by any clean-token posterior under the forward kernel. The score-entropy loss has the correct population optimum but does not enforce this constraint away from it. In a trained pure-uniform SEDD checkpoint, roughly one quarter of complete score vectors violate the coordinate box, while more than half lie inside it yet remai
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- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-xl-base-1.0 →
“Fuzzy title match (0.73): “Mean-to-Score Discrete Diffusion: Posterior-Mean Denoisers f” ≈ “stabilityai/stable-diffusion-xl-base-1.0””
- FuzzySimilar title/name (fuzzy) · 59%CompVis/stable-diffusion-v1-4 →
“Fuzzy title match (0.73): “Mean-to-Score Discrete Diffusion: Posterior-Mean Denoisers f” ≈ “CompVis/stable-diffusion-v1-4””
- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-3.5-large →
“Fuzzy title match (0.73): “Mean-to-Score Discrete Diffusion: Posterior-Mean Denoisers f” ≈ “stabilityai/stable-diffusion-3.5-large””
- FuzzyOverlapping authors or contributors · 62%affaan-m/ECC →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%BerriAI/litellm →
“Shared author/contributor keys: jiang”
- LinkedLinked via arxiv author · 85%Jingyuan Li →
“Mean-to-Score Discrete Diffusion: Posterior-Mean Denoisers for Score Entropy”
- LinkedLinked via arxiv author · 85%Xiaoyi Jiang →
“Mean-to-Score Discrete Diffusion: Posterior-Mean Denoisers for Score Entropy”