English(EN) TensorSSM: Memorizing Spatial Position in Dynamic State Space for Tensor Core Generation

TensorSSM 使用动态状态空间模型进行张量压缩

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员推出了一种新颖的隐式神经表示（INR）方法 TensorSSM，用于函数式张量分解。该方法利用动态状态空间模型（SSM）来记忆高维张量中的空间位置，解决了现有顺序展平方法的局限性。TensorSSM 通过从粗到细地编码空间标记并扩展 SSM 的状态空间以实现跨模态交互，从而捕获复杂的跨模态空间依赖性，提高了数据压缩的准确性和效率。 AI

影响这种新方法有望提高 AI 应用中高维张量数据更高效的存储和处理。

排序理由该集群包含一篇详细介绍张量分解新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Sheng Zhang, Xinyi Wang, Maolin Wang, Xiangyu Zhao · 2026-11-02 05:00

TensorSSM: Memorizing Spatial Position in Dynamic State Space for Tensor Core Generation

arXiv:2610.40871v1 Announce Type: new Abstract: High-dimensional tensors are ubiquitous in real-world applications but lead to massive storage overhead. Functional tensor decomposition addresses this by employing neural networks to map element coordinates to tensor cores, signifi…

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TensorSSM: Memorizing Spatial Position in Dynamic State Space for Tensor Core Generation

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