AINeutralarXiv – CS AI · 11h ago6/10
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Discrete State Diffusion Models: A Sample Complexity Perspective
Researchers present the first theoretical framework establishing sample complexity bounds for discrete-state diffusion models, a fundamental gap in AI research. The work provides an $\widetilde{\mathcal{O}}(\epsilon^{-2})$ sample complexity bound and decomposes score estimation error into four components, advancing understanding of how these models can be trained efficiently for text and combinatorial applications.