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English(EN) COMPOT: Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers Compression

新的 COMPOT 框架提供高效的 Transformer 模型压缩

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 COMPOT，一个用于压缩 Transformer 模型的新型无训练框架。该方法利用校准数据集通过正交字典估计稀疏权重分解，从而实现高效的闭式更新。COMPOT 还采用动态分配策略，自适应地将压缩率分配到不同层，旨在与现有的低秩和稀疏基线相比，实现更好的质量-压缩权衡。该框架兼容训练后量化，以实现极高的压缩水平。 AI

排序理由 该集群描述了 arXiv 上学术论文中提出的一种新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的 COMPOT 框架提供高效的 Transformer 模型压缩

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该集群描述了 arXiv 上学术论文中提出的一种新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Denis Makhov, Dmitriy Shopkhoev, Magauiya Zhussip, Ammar Ali, Baher Mohammad, Stamatios Lefkimmiatis ·

    COMPOT：用于 Transformer 压缩的校准优化矩阵普罗克鲁斯特正交化

    arXiv:2602.15200v3 Announce Type: replace Abstract: Post-training compression of transformer models commonly relies on truncated singular value decomposition (SVD) strategies leading to low-rank approximations of the original model weights. However, the underlying single shared s…

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