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English(EN) What Does a Discrete Diffusion Model Learn?

新论文通过“Oracle Distance”定理对离散扩散模型进行理论化

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新论文探讨了离散扩散模型的理论基础，提出了“Oracle Distance”定理。该定理将负证据下界（ELBO）等同于数据熵加上预言机和学习到的逆向过程之间的路径KL散度。该研究确定了优化器的三个精确坐标——去噪器、空腔和分数——并提供了它们之间的转换方法，统一了MDM和UDM等各种现有损失函数。 AI

影响 为理解和优化离散扩散模型提供了一个统一的理论框架。

排序理由 该集群包含一篇在arXiv上发表的学术论文，详细介绍了机器学习领域的理论研究。

在 arXiv stat.ML 阅读 →

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新论文通过“Oracle Distance”定理对离散扩散模型进行理论化

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该集群包含一篇在arXiv上发表的学术论文，详细介绍了机器学习领域的理论研究。
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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Rodrigo Casado Noguerales, Bernhard Sch\"olkopf, Thomas Hofmann, Aran Raoufi ·

    离散扩散模型学习了什么？

    arXiv:2607.05381v1 Announce Type: cross Abstract: What does a discrete diffusion model learn: a denoiser, a score ratio, or a bridge plug-in predictor? At the level of jump rates, these are one object in different coordinates, and reading a neural network in the wrong coordinate …

  2. arXiv stat.ML TIER_1 English(EN) · Aran Raoufi ·

    离散扩散模型学习了什么？

    What does a discrete diffusion model learn: a denoiser, a score ratio, or a bridge plug-in predictor? At the level of jump rates, these are one object in different coordinates, and reading a neural network in the wrong coordinate changes the process being trained and sampled. Sta…

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