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English(EN) JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures

JEPA-DNA 框架通过潜在语义接地增强基因组基础模型

研究人员开发了 JEPA-DNA,一个用于训练基因组基础模型的新框架。该方法将联合嵌入预测架构 (JEPA) 与现有的生成目标相结合,以改进这些模型学习生物序列的方式。通过专注于预测潜在空间中的功能表示,JEPA-DNA 增强了超越简单标记重建的语义理解。该框架在 17 项基因组基准任务上展示了最先进的性能,在线性探测和零样本评估中均显示出持续的提升。 AI

影响 增强了基因组模型中的语义理解,可能改进药物发现和生物学研究。

排序理由 该集群包含一篇详细介绍新 AI 模型训练框架的研究论文。[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 →

JEPA-DNA 框架通过潜在语义接地增强基因组基础模型

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该集群包含一篇详细介绍新 AI 模型训练框架的研究论文。[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) · Ariel Larey, Elay Dahan, Amit Bleiweiss, Raizy Kellerman, Guy Leib, Omri Nayshool, Dan Ofer, Tal Zinger, Dan Dominissini, Gideon Rechavi, Nicole Bussola, Simon Lee, Shane O'Connell, Dung Hoang, Marissa Wirth, Alexander W. Charney, Nati Daniel, Yoli Shavit ·

    JEPA-DNA:通过联合嵌入预测架构实现基因组基础模型的接地

    arXiv:2602.17162v2 Announce Type: replace Abstract: Genomic Foundation Models (GFMs) typically rely on Masked Language Modeling (MLM) or Next-Token Prediction (NTP) to learn the "Laws of Nature". While effective at capturing local syntax, these generative paradigms prioritize tok…