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Yuanzhong Xu

Yuanzhong Xu has 2 papers in our library, in 2021–2022, most of them on Language models & pretraining; often with Yanping Huang, Adams Wei Yu, Andrew M. Dai.

2 papers in our library · 2021–2022

OpenAlex · ORCID · Wikidata · h-index 27 · 58 works · 3,086 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 6,245 times in our library · 5 more in our library without a page yet · ⭐ Paper Stars ranking: #3212

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (2021 · 496 citations)
    Scaling dense language models such as GPT-3 improves few-shot learning, but the compute and energy needed to train and serve them grow steeply.
  2. Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning (2022 · 279 citations)
    Training models with billions of parameters requires combining data, operator and pipeline parallelism. Until now, engineers either designed that combination by hand for a specific model and cluster, as with Megatron-LM's "3D parallelism", or used automatic tools that search only one kind of parallelism. Alpa takes a…

All works

7 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding ↗arXiv:2006.16668 · no summary yetDmitry Lepikhin et al.2020 · 894 citations
  2. GLaM: Efficient Scaling of Language Models with Mixture-of-ExpertsarXiv:2112.06905Nan Du, Yanping Huang, Andrew M. Dai, et al.2021 · 496 citations
  3. Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep LearningOSDI 2022Lianmin Zheng, Zhuohan Li, Hao Zhang, et al.2022 · 279 citations
  4. BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition ↗IEEE Journal of Selected Topics in Signal Processing · no summary yetYu Zhang et al.2022 · 180 citations
  5. GSPMD: General and Scalable Parallelization for ML Computation Graphs ↗arXiv (Cornell University) · no summary yetYuanzhong Xu et al.2021 · 119 citations
  6. Exploring the limits of Concurrency in ML Training on Google TPUs ↗arXiv (Cornell University) · no summary yetSameer Kumar et al.2020 · 14 citations
  7. Learning to Skip for Language Modeling ↗arXiv (Cornell University) · no summary yetZeng, Dewen et al.2023 · 8 citations

 Co-authors here

12 of 16 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 6,245 times (of the 149,439 papers whose bibliographies we read); 1,290 citing papers with a page here →.

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