The robustness of Internet of Things (IoT) topologies, which measures a network’s tolerance to node removals, is critical to maintaining stable network connectivity. Existing robustness optimization methods mainly rely on search-based heuristics, which suffer from path dependence and often converge to suboptimal topolo...
Ji-Qiao Li, Ning Chen, Xiao-Bo Zhou et al.· IEEE Transactions on Network...· 0 citations
The Noise Equivalent Hypothesis is proposed: the noise inherent to the weight quantization process plays a role functionally analogous to the stochastic gradient variance present in standard SGD optimization, and it is theoretically prove that just as a decaying learning rate is essential for convergence, an ascending...
Hong Huang, Jia-Xun Ye, Jin-Hai Yang et al.· Proceedings of the 32nd ACM...· 0 citations
Training Large Foundation Models (LFMs), including Large Language Models and Vision-Language Models, on massive distributed GPU clusters is increasingly bottlenecked by communication overhead. While frameworks like ZeRO++ employ static quantization to reduce communication volume, they suffer from a rigid trade-off: agg...
Hong Huang, Jiaxun Ye, Jinhai Yang et al.· Proceedings of the 32nd ACM...· 0 citations
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