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English(EN) Diffusion models as plug-and-play priors

扩散模型适用于复杂的推理和控制任务 · 已追踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了新的方法，将扩散模型用作复杂推理任务中的灵活先验。第一篇论文探讨了通过对去噪网络进行迭代微分，将扩散模型改编为条件生成和图像分割等任务的即插即用模块。第二篇论文引入了一种名为相对轨迹平衡的数据无关学习目标，该目标源于生成流网络视角，能够对扩散模型中难以处理的后验进行摊销采样。这种方法在视觉、语言和控制任务中显示出潜力，包括在离线强化学习中取得最先进的成果。 AI

影响 扩散模型的这些进步可能实现更复杂的生成式AI应用，并提高控制和推理任务的性能。

排序理由 该集群包含两篇在arXiv上发表的学术论文，详细介绍了关于扩散模型的新研究。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

扩散模型适用于复杂的推理和控制任务 · 已追踪2个来源

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该集群包含两篇在arXiv上发表的学术论文，详细介绍了关于扩散模型的新研究。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Alexandros Graikos, Esmeralda S. Whitammer, Nebojsa Jojic, Dimitris Samaras ·

    扩散模型作为即插即用先验

    arXiv:2206.09012v4 Announce Type: replace Abstract: We consider the problem of inferring high-dimensional data $\mathbf{x}$ in a model that consists of a prior $p(\mathbf{x})$ and an auxiliary differentiable constraint $c(\mathbf{x},\mathbf{y})$ on $x$ given some additional infor…

  2. arXiv cs.LG TIER_1 English(EN) · Siddarth Venkatraman, Moksh Jain, Luca Scimeca, Minsu Kim, Marcin Sendera, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, Alexandre Adam, Jarrid Rector-Brooks, Yoshua Bengio, Glen Berseth, Esmeralda S. Whitammer ·

    用于视觉、语言和控制的扩散模型中难解推理的摊销

    arXiv:2405.20971v3 Announce Type: replace Abstract: Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in downstream tasks poses an intractable posterior inference problem. This paper studies…

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