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English(EN) Recovering linear images of sparse signals from indirect observations

两篇论文探讨稀疏信号恢复技术 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文探讨了从间接观测中恢复稀疏信号的挑战和方法。第一篇论文由Anatoli Juditsky撰写，侧重于开发和分析在不对感知矩阵做出限制性假设的情况下恢复稀疏信号线性图像的技术。第二篇论文由Youssef Chaabouni撰写，研究了使用稀疏和稀疏化测量进行稀疏恢复的充分条件，确定了信息论阈值和计算增益。 AI

影响 这些论文为信号处理和机器学习的基础研究做出了贡献，可能影响未来依赖于从嘈杂或不完整观测中有效恢复数据的AI模型开发。

排序理由 两篇在arXiv上发表的关于信号恢复技术的学术论文。

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 →

两篇论文探讨稀疏信号恢复技术 · 跟踪2个来源

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两篇在arXiv上发表的关于信号恢复技术的学术论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Anatoli Juditsky, Arkadi Nemirovski ·

    从间接观测中恢复稀疏信号的线性图像

    arXiv:2609.06182v1 Announce Type: cross Abstract: In this paper, we develop and analyze techniques for recovering a linear image $Bx$ of an unknown signal $x$ from indirect noisy observation $\omega=Ax+\xi$. It is {\em a priori} known that $x\in \cX$, a given convex compact set, …

  2. arXiv cs.LG TIER_1 English(EN) · Youssef Chaabouni, David Gamarnik ·

    稀疏性的代价：使用稀疏和稀疏化测量进行稀疏恢复的充分条件

    arXiv:2509.01809v2 Announce Type: replace-cross Abstract: We consider the problem of support recovery for sparse binary signals from noisy linear measurements. For sparse Gaussian measurement matrices we identify sufficient conditions on the minimal sample size for maximum-likeli…

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