.png){kind=icon}
返回
Deep neural networks compression learning based on multiobjective evolutionary algorithms
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2019.10.053.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This work addresses the problem of deep neural network compression, which is a promising technique to reduce the number of network parameters and/or speed up the network evaluation process significantly. Most of the existing methods rely on domain experts' experience for the selection of hyperparameters such as the rank and sparsity ratio of the weight matrix in order to get an appropriate compression result without serious performance downgrade. However, they usually suffer from heavy computational loads due to the large number of tests in revealing the best hyperparameters. In this work, we propose an efficient approach to network compression from the perspective of multiobjective evolution. The contributions in the paper are twofolds: (1) We build a multiobjective compression learning model that considers the model classification error rate and compression rate as two objectives in the optimization, which can provide a compromise of the tradeoffs between these two objectives. (2) A mechanism for approximate compressed model generation is devised in the framework of expensive multiobjective optimization, which is able to reduce the high model training costs involved in the optimization process. Experiments are carried out to confirm the superiority of the proposed algorithm. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Neural network compression
Evolutionary optimization
Pareto-optimal solutions
Expensive optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Combining segmental semi-Markov models with neural networks for protein secondary structure prediction.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏分享收藏
分享收藏Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
PREAI
分享收藏A Revisit to Supercapacitor Capacitance Measurement Method 1A of IEC 62391-1IEC 62391超级电容器电容测量方法1A的再审视
PREAI
分享收藏
查看更多内容