26 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Shanghai Jiao Tong UniversityCN2 papers
- Beijing Academy of Artificial IntelligenceCN1 paper
- Carnegie Mellon UniversityUS1 paper
- China Agricultural UniversityCN1 paper
- Department of Physics, Mathematics and InformaticsBY1 paper
- Electronics and Telecommunications Research InstituteKR1 paper
- Ghent UniversityBE1 paper
- KTH Royal Institute of TechnologySE1 paper
- Peking UniversityCN1 paper
- Radboud University NijmegenNL1 paper
- Tsinghua–Berkeley Shenzhen InstituteCN1 paper
- Tsinghua UniversityCN1 paper
Showing cs.DCShow all
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cs.DC2026
MegaScale-Omni: A Hyper-Scale, Workload-Resilient System for MultiModal LLM Training in Production
Chunyu Xue, Yangrui Chen, Jianyu Jiang +14
As the foundational component of versatile AI applications, training an multimodal large language model (MLLM) relies on multimodal datasets with dynamic modality mixture proportio…
cs.DC2026
LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training
Xinyi Liu, Yujie Wang, Fangcheng Fu +4
Expert parallelism is vital for effectively training Mixture-of-Experts (MoE) models, enabling different devices to host distinct experts, with each device processing different inp…