研究人员开发了 LUD-MSR，一个新颖的潜在变量概率框架，旨在从边际观测中建模联合分布。该方法通过仅使用边际数据优化证据下界来解决此类问题的固有不适定性。该框架引入了多尺度图像表示 (MSR) 映射，以平衡域一致性和信息保留，并在冷冻电子显微镜去噪基准实验中证明了其有效性。 AI
影响 这项研究通过提供一种更稳健的方法从有限的观测中学习复杂数据分布，有可能推动生成建模技术的发展。
排序理由 该集群包含一篇详细介绍联合分布建模新方法的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2607.08198v1 Announce Type: new Abstract: This paper studies the problem of learning a joint distribution from marginal observations, which is inherently ill-posed due to the ambiguity of feasible couplings. We propose LUD-MSR, a latent-variable probabilistic framework that…
This paper studies the problem of learning a joint distribution from marginal observations, which is inherently ill-posed due to the ambiguity of feasible couplings. We propose LUD-MSR, a latent-variable probabilistic framework that models the joint distribution via auxiliary rep…