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English(EN) Learning Permutation Distributions via Reflected Diffusion on Ranks

新的软秩扩散模型增强排列学习

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为软秩扩散 (Soft-Rank Diffusion) 的新扩散模型，用于学习排列上的概率分布。该方法通过使用软秩前向过程，将离散秩松弛为连续表示以获得更平滑的轨迹，从而改进了现有技术。该模型还结合了上下文广义 Plackett-Luce 去噪器以增强表达能力。实验表明，Soft-Rank Diffusion 在序列任务和长序列上优于之前的扩散基线。 AI

影响 引入了一种新颖的扩散模型，可以提高机器学习中基于排列的任务的性能。

排序理由 发表了一篇关于新型排列分布扩散模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的软秩扩散模型增强排列学习

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发表了一篇关于新型排列分布扩散模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sizhuang He, Yangtian Zhang, Shiyang Zhang, David van Dijk ·

    通过反射式扩散学习排列分布

    arXiv:2603.17353v2 Announce Type: replace-cross Abstract: The finite symmetric group S_n provides a natural domain for permutations, yet learning probability distributions on S_n is challenging due to its factorially growing size and discrete, non-Euclidean structure. Recent perm…

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