160 citations · 314 across the 3 of their papers we have counts for
3 papers
cs.SI2021
Learning Large-scale Network Embedding from Representative Subgraph
Junsheng Kong, Weizhao Li, Ben Liao +6
We study the problem of large-scale network embedding, which aims to learn low-dimensional latent representations for network mining applications. Recent research in the field of n…
cs.CV2020★ 154 cited
Discrimination-aware Network Pruning for Deep Model Compression
Jing Liu, Bohan Zhuang, Zhuangwei Zhuang +4
We study network pruning which aims to remove redundant channels/kernels and hence speed up the inference of deep networks. Existing pruning methods either train from scratch with…
cs.CV2018★ 160 cited
Discrimination-aware Channel Pruning for Deep Neural Networks
Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang +5
Channel pruning is one of the predominant approaches for deep model compression. Existing pruning methods either train from scratch with sparsity constraints on channels, or minimi…