English(EN) DADP: Dynamic Activity-Dependent Pruning, A Reverse Hebbian-Inspired Structural Pruning Method

受生物学启发的剪枝新方法DADP可减小神经网络尺寸

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员推出了一种名为动态依赖激活的剪枝（DADP）的新型方法，用于减小神经网络的尺寸。DADP受生物可塑性启发，通过测量累积的突触前激活和突触后误差梯度来评估连接的重要性。该方法允许使用单一全局阈值在网络层之间动态分配稀疏性，从而实现神经元和通道级别的剪枝。DADP在MLP、VGG-16、ResNet-18、BiLSTM-CRF和MiniBERT等多种架构上，其性能与Magnitude、SNIP和RigL等现有方法相当或更优，同时实现了高稀疏度。 AI

影响这种新的剪枝方法有望通过降低计算和内存需求，实现更高效的AI模型。

排序理由该集群包含一篇详细介绍新型神经网络剪枝方法的论文。[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) · Bhushan Deshpande · 2026-10-09 04:00

DADP：动态依赖活动修剪，一种受反赫布启发结构修剪方法

arXiv:2610.11853v1 Announce Type: new Abstract: Modern neural networks are heavily over-parameterized. This redundancy incurs substantial compute and memory overhead during training and inference. Existing pruning methods rely on post-hoc magnitude thresholds or static initializa…

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DADP：动态依赖活动修剪，一种受反赫布启发结构修剪方法

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