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克雷斯普:DNN的模块化分散化,以提高修剪性能和模型可解释性

Tianyu Kang1, Wei Ding1, Ping Chen1

  • 1University of Massachusetts Boston, United States of America.

Neural networks : the official journal of the International Neural Network Society
|January 10, 2024
PubMed
概括

集群限制的冗余性极度稀疏性修剪 (CRESPR) 将神经网络修剪成模块化单元,以提供高效,可解释的深度学习模型. 这种方法提高了高修剪比率的性能,为模型决策提供了更好的洞察力.

关键词:
模型的解释性 模型的解释性神经网络的修剪神经网络的修剪

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