@inproceedings{280789f447ff4a35a210f2982a2174da,
title = "Novel Pruning of Dendritic Neuron Models for Improved System Implementation and Performance",
abstract = "Pruning is widely used for neural network model compression. It removes redundant links from a weight tensor to lead to smaller and more efficient neural networks for system implementation. A compressed neural network can enable faster run and reduced computational cost in network training. In this paper, a novel pruning method is proposed for a dendritic neuron model (DNM). It calculates the significance of each DNM dendrite. The calculated significance is expressed numerically and a dendrite whose significance is lower than a pre-set threshold is removed. Experimental results verify that it obtains superior performance over the existing one in terms of both accuracy and computational efficiency.",
keywords = "Complex systems, Dendritic Neuron Model (DNM), Machine learning, Neural network, Pruning",
author = "Xiaohao Wen and Mengchu Zhou and Xudong Luo and Lukui Huang and Ziyue Wang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 ; Conference date: 17-10-2021 Through 20-10-2021",
year = "2021",
doi = "10.1109/SMC52423.2021.9659103",
language = "English (US)",
series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1559--1564",
booktitle = "2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021",
address = "United States",
}