5 citations · 10 across the 5 of their papers we have counts for
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cs.LG2024
Improved Quantization Strategies for Managing Heavy-tailed Gradients in Distributed Learning
Guangfeng Yan, Tan Li, Yuanzhang Xiao +2
Gradient compression has surfaced as a key technique to address the challenge of communication efficiency in distributed learning. In distributed deep learning, however, it is obse…
cs.LG2024
Truncated Non-Uniform Quantization for Distributed SGD
Guangfeng Yan, Tan Li, Yuanzhang Xiao +2
To address the communication bottleneck challenge in distributed learning, our work introduces a novel two-stage quantization strategy designed to enhance the communication efficie…
cs.IR2024★ 5 cited
Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation
Sichun Luo, Yuxuan Yao, Bowei He +9
Conventional recommendation methods have achieved notable advancements by harnessing collaborative or sequential information from user behavior. Recently, large language models (LL…