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SHUFFLESPARSE learned permutations boost structured sparse network accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SHUFFLESPARSE, a novel permutation primitive designed to enhance structured weight sparsity in neural networks. This method aims to close the accuracy gap between structured and unstructured sparse training, particularly at high sparsity levels. By learning a permutation matrix alongside the weight matrix, SHUFFLESPARSE has shown significant improvements in accuracy for models like ViT-B16 and GPT-2, and notably boosts the zero-shot accuracy of LLaMA-2 7B by 4.6 points. AI

IMPACT This method could enable more efficient deployment of large models by improving structured sparsity techniques.

RANK_REASON The cluster is a research paper detailing a new method for improving neural network sparsity. [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 →

SHUFFLESPARSE learned permutations boost structured sparse network accuracy

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The cluster is a research paper detailing a new method for improving neural network sparsity. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Abhishek Tyagi, Arjun Iyer, Liam Young, William H Renninger, Christopher Kanan, Yuhao Zhu ·

    SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks

    arXiv:2510.14812v2 Announce Type: replace Abstract: Structured weight sparsity accelerates training and inference on modern GPUs, but it trails unstructured dynamic sparse training (DST) in accuracy especially at extreme sparsity. We pinpoint the reason for this difference in per…

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