Web: http://arxiv.org/abs/2206.07311

June 16, 2022, 1:10 a.m. | Zhangheng Li, Tianlong Chen, Linyi Li, Bo Li, Zhangyang Wang

cs.LG updates on arXiv.org arxiv.org

With the rapid development of deep learning, the sizes of neural networks
become larger and larger so that the training and inference often overwhelm the
hardware resources. Given the fact that neural networks are often
over-parameterized, one effective way to reduce such computational overhead is
neural network pruning, by removing redundant parameters from trained neural
networks. It has been recently observed that pruning can not only reduce
computational overhead but also can improve empirical robustness of deep neural
networks (NNs), …

arxiv lg networks neural neural networks pruning robustness

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