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English(EN) Making Single-Cell Data Distillation Auditable: Traceable Real-Cell Coresets via Discrete Min-Max Selection

新方法改进了可审计的单细胞数据蒸馏

研究人员开发了可追溯单细胞数据蒸馏的新方法,旨在降低存储和重用大型单细胞数据集进行模型训练的成本和复杂性。提出的技术 Fixed-CF 和 Minmax-CF 专注于在固定预算内保留原始细胞标识符和基因符号,从而能够将合成表达谱追溯到分析过的细胞。特别是 Minmax-CF 利用熵正则化的离散最小-最大问题来提高在各种数据偏移下的性能,并显著加快了计算速度。 AI

影响 提高了在大型生物数据集上训练 AI 模型的效率和可审计性。

排序理由 该集群包含一篇学术论文,详细介绍了基因组学中数据蒸馏的新方法。[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 →

新方法改进了可审计的单细胞数据蒸馏

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该集群包含一篇学术论文,详细介绍了基因组学中数据蒸馏的新方法。[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) · Yaodi Luo, Peize He, Bowen Han, Lingbei Mengg ·

    使单细胞数据蒸馏可审计:通过离散极小极大选择实现可追溯的真实细胞核集

    arXiv:2607.19426v1 Announce Type: cross Abstract: Single-cell datasets are increasingly costly to store, audit, and reuse for model training. Dimensionality reduction and dataset distillation can reduce this burden, but conventional distillation methods often produce synthetic ex…