61 citations · 133 across the 9 of their papers we have counts for
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cs.LG2023★ 17 cited
ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation
Zhewei Yao, Xiaoxia Wu, Cheng Li +2
Post-training quantization (PTQ) has emerged as a promising technique for mitigating memory consumption and computational costs in large language models (LLMs). However, a systemat…
cs.LG2022★ 3 cited
BiFeat: Supercharge GNN Training via Graph Feature Quantization
Yuxin Ma, Ping Gong, Jun Yi +4
Graph Neural Networks (GNNs) is a promising approach for applications with nonEuclidean data. However, training GNNs on large scale graphs with hundreds of millions nodes is both r…