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cs.LG2025
Scaling Up Data Parallelism in Decentralized Deep Learning
Bing Xie, Junqi Yin, Zhenyu Zhou +2
Although it has been extensively explored in theory, decentralized learning is not yet green-lighted for production use, largely due to a lack of stability, scalability, and genera…
cs.LG2025★ 3 cited
Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training
Wesley Brewer, Murali Meena Gopalakrishnan, Matthias Maiterth +12
With the end of Moore's law and Dennard scaling, efficient training increasingly requires rethinking data volume. Can we train better models with significantly less data via intell…