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SCAP:Stiefel流形路由的双线性SPD层
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2026年10月5日,arXiv(Statistics Machine Learning,一手来源)发布论文《突破容量上限:在Stiefel流形上为双线性SPD层做路由》。论文提出SCAP(Stiefel Cross-Attention Pool),用K个专家池经cross-attention组合成样本专属双线性映射,替代SPDNet中堆叠BiMap与ReEig的固定滤波结构。这是目前该事件唯一报道,尚无后续验证或独立评论。
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Oct 5, 2026
- arXiv · Statistics Machine Learning突破容量上限:在 Stiefel 流形上为双线性 SPD 层做路由
论文提出 SCAP(Stiefel Cross-Attention Pool),用 K 个专家池经 cross-attention 组合成样本专属双线性映射,替代 SPDNet 中堆叠 BiMap 与 ReEig 的固定滤波结构。
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