English(EN) Learning Functional Subspaces for Neural Network Compression

新方法学习子空间以实现有效的神经网络压缩

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了一种名为可学习子空间投影（LSP）的新方法来压缩神经网络，特别是Transformer。与使用局部标准的先前技术不同，LSP针对全局目标（如与原始模型输出分布的KL散度或训练损失）进行端到端子空间优化。这种方法通过防止网络深度中的误差累积，实现了更有效的压缩，尤其是在更高压缩率下。LSP在OPT、Qwen3、Llama-2和ViT-B/16等模型上表现出优越的性能，在显著的压缩水平下实现了比基线方法更好的困惑度和准确性。 AI

影响该方法可以通过降低大型语言模型的计算和内存需求，从而实现更高效的部署。

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在 arXiv cs.CL 阅读 →

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Massimo Bini, Anders Christensen, Stephan Alaniz, Judah Goldfeder, Ole Winther, Yann LeCun, Ravid Shwartz-Ziv, Zeynep Akata · 2026-10-01 04:00

学习函数式子空间以实现神经网络压缩

arXiv:2609.40127v1 Announce Type: cross Abstract: Modern transformers pair impressive capabilities with substantial memory and compute demands. Low-rank weight factorization reduces both while keeping the matrices dense, and thus efficient on standard hardware. Existing methods, …

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学习函数式子空间以实现神经网络压缩

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