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相关概念视频

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相关实验视频

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DDK:基于可微分搜索和BERT的递归知识蒸的动态结构修剪.

Zhou Zhang1, Yang Lu2, Tengfei Wang1

  • 1School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China.

Neural networks : the official journal of the International Neural Network Society
|February 17, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了DDK,这是一种结合动态结构修剪和递归知识蒸的方法,用于压缩大型预训练语言模型,如BERT. 这种方法显著减少了实际NLP应用的计算需求.

关键词:
可以区分的方法.知识的蒸知识的蒸.模型的压缩压缩.网络修剪是为了修剪网络.预先训练有素的模型.

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科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 大规模预训练模型 (例如BERT) 在NLP中表现出色,但需要大量的计算资源.
  • 由于存储和处理需求,高参数数量阻碍了实际部署.

研究的目的:

  • 为大型预训练语言模型提出一种新的压缩技术.
  • 提高像BERT这样的模型的效率和部署性.

主要方法:

  • 引入了DDK,一种结合动态结构修剪和递归知识蒸的方法.
  • 利用可区分搜索来优化前层道和自我注意头.
  • 实施递归知识蒸与适应权重,从中间层提取特征.

主要成果:

  • 该DDK方法成功地压缩和加速了BERT模型.
  • 与现有方法相比,GLUE基准标准的实验结果显示出更高的性能.
  • 除分析验证了拟议的修剪和蒸策略的有效性.

结论:

  • DDK 方法为压缩大型预训练语言模型提供了有效的解决方案.
  • 这种方法解决了资源密集型NLP模型的实际部署挑战.
  • DDK实现了最先进的性能,同时显著提高了模型效率.