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Creators/Authors contains: "Geng, T"

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  1. ; ; ; ; ; ; ; ; ; ; et al (, Conference on Neural Information Processing Systems.)
    Free, publicly-accessible full text available December 2, 2026
  2. ; ; ; ; ; (, International Conference on Machine Learning (ICML))
    Free, publicly-accessible full text available July 1, 2026
  3. ; ; ; ; ; ; (, International Conference on Learning Representations (ICLR))
    Free, publicly-accessible full text available May 1, 2026
  4. ; ; ; ; ; ; ; ; ; (, International Conference on Learning Representations (ICLR))
    Free, publicly-accessible full text available May 1, 2026
  5. ; ; ; ; ; ; ; (, Advances in Neural Information Processing Systems (NeurIPS))
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; ; ; (, NeurIPS)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; ; ; ; ; et al (, International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; ; ; ; ; (, The Third Learning on Graphs Conference (LoG))
    Accepted Manuscript Full Text Available
  9. ; ; ; (, IEEE)
    Distributed deep learning framework tools should aim at high efficiency of training and inference of distributed exascale deep learning algorithms. There are three major challenges in this endeavor: scalability, adaptivity and efficiency. Any future framework will need to be adaptively utilized for a variety of heterogeneous hardware and network environments and will thus be required to be capable of scaling from single compute node up to large clusters. Further, it should be efficiently integrated into popular frameworks such as TensorFlow, PyTorch, etc. This paper proposes a dynamically hybrid (hierarchy) distribution structure for distributed deep learning, taking advantage of flexible synchronization on both centralized and decentralized architectures, implementing multi-level fine-grain parallelism on distributed platforms. It is scalable as the number of compute nodes increases, and can also adapt to various compute abilities, memory structures and communication costs. 
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    Accepted Manuscript Full Text Available
  10. ; ; ; ; (, FTAC)
    Accepted Manuscript Full Text Available