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English(EN) OSDN: Improving Delta Rule with Provable Online Preconditioning in Linear Attention

OSDN 通过在线预处理改进线性注意力

研究人员推出了一种新颖的方法 OSDN,该方法通过结合可证明的在线预处理来增强线性注意力机制。该技术通过超梯度反馈在线更新的对角线预处理器来增强 Delta 规则。OSDN 有效地按特征缩放写入端键,在不增加显着开销的情况下保留了 DeltaNet 的高效并行流水线。该方法在上下文内回忆任务中表现出改进的性能,在各种参数规模下均显示出比现有方法显着的优势。 AI

影响 引入了一种改进线性注意力模型中上下文内回忆的新技术,有可能增强其处理长序列的能力。

排序理由 该集群包含一篇详细介绍改进线性注意力机制的新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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OSDN 通过在线预处理改进线性注意力

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该集群包含一篇详细介绍改进线性注意力机制的新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yinyu Ye ·

    OSDN:通过线性注意力中的可证明在线预处理改进 Delta 规则

    Linear attention and state-space models offer constant-memory alternatives to softmax attention, but often struggle with in-context associative recall. The Delta Rule mitigates this by writing each token via one step of online gradient descent. However, its step size relies on a …