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TensorSSM uses dynamic state space models for tensor compression

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced TensorSSM, a novel Implicit Neural Representation (INR) designed for functional tensor decomposition. This method utilizes a dynamic State Space Model (SSM) to memorize spatial positions within high-dimensional tensors, addressing limitations of existing sequential flattening approaches. TensorSSM captures complex cross-mode spatial dependencies by encoding spatial tokens from coarse to fine and extending the SSM's state space for inter-mode interactions, leading to improved data compression accuracy and efficiency. AI

IMPACT This new method could lead to more efficient storage and processing of high-dimensional tensor data in AI applications.

RANK_REASON The cluster contains a research paper detailing a new method for tensor decomposition. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

TensorSSM uses dynamic state space models for tensor compression

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The cluster contains a research paper detailing a new method for tensor decomposition. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sheng Zhang, Xinyi Wang, Maolin Wang, Xiangyu Zhao ·

    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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