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English(EN) Structured Neuron Pruning in Deep Neural Networks Using Multi-Armed Bandits

多臂老虎机优化深度神经网络的结构化剪枝

研究人员开发了一种新颖的深度神经网络结构化剪枝框架,该框架利用多臂老虎机(MAB)算法来移除整个神经元。该方法将每个神经元视为老虎机问题中的一个“臂”,暂时屏蔽它以衡量对损失函数的影响,然后更新其移除奖励估计。在图像、文本和推理任务上的评估表明,基于MAB的剪枝,特别是使用UCB1和Thompson Sampling策略,可以有效地减小模型尺寸,并且通常优于未剪枝模型和其他剪枝技术。 AI

影响 引入了一种新颖、计算上可行的方法来进行结构化模型缩减,可以提高性能和效率。

排序理由 该集群包含一篇详细介绍深度神经网络新优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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多臂老虎机优化深度神经网络的结构化剪枝

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该集群包含一篇详细介绍深度神经网络新优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Salem Ameen, Sunil Vadera ·

    使用多臂老虎机对深度神经网络进行结构化剪枝

    arXiv:2606.07615v1 Announce Type: cross Abstract: Deep neural networks often contain redundant hidden units. Removing individual weights can reduce parameter count, but unstructured sparsity is not always easy to exploit in standard dense implementations. This paper develops a st…