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Paper proposes new sparse tensor representation and scattering theorem

By PulseAugur Editorial · [1 sources] ·

This paper introduces a novel method for representing sparse tensors, establishing a theoretical framework for tensor data scattering techniques used in deep learning. It presents a theorem explaining the impossibility of slicing in tensor data scattering, crucial for performance analysis and accelerator optimization. The research also offers a formula to measure sparsity efficiency and a Python implementation. AI

IMPACT Introduces theoretical framework and implementation for efficient sparse tensor handling in deep learning.

RANK_REASON The item is an academic paper submitted to arXiv detailing theoretical computer science research. [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 →

Paper proposes new sparse tensor representation and scattering theorem

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The item is an academic paper submitted to arXiv detailing theoretical computer science research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Wuming Pan ·

    Tensor Data Scattering and the Impossibility of Slicing Theorem

    arXiv:2012.01982v3 Announce Type: replace Abstract: This paper proposes a standard way to represent sparse tensors. A broad theoretical framework for tensor data scattering methods used in various deep learning frameworks is established. This paper presents a theorem that is very…

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