From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 97 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.

📚 Shelves 6Arts and Humanities · 2 works Computer Science · 2 works Artificial Intelligence · 2 works Computer Vision and Pattern Recognition · 2 works Hardware and Architecture · 2 works Language and Linguistics · 2 works

Works by year

2020s

20252 papers

Papers

The most cited first (OpenAlex and our library).

Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs (2025 · 8 citations)
Training MoE language models with nearly a trillion parameters is hard: the hardware is underused because the set of experts each token activates changes at runtime, and the scale strains communication and memory.
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity (2025 · 4 citations)
Sparse Mixture-of-Experts LLMs are cheap per token. However, when experts are spread across accelerators, uneven routing overloads some devices, and the slowest device limits overall speed. The Pangu Team at Huawei proposes Mixture of Grouped Experts (MoGE). Experts are split into equal, non-overlapping groups…

Co-authors here

11 co-authors with a page, the most papers together first.

Cited by

Papers of our library cite their works 11 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated 1 November 2026

This page in Markdown → https://md.research.elamur.ai/free/en/authors/hang-zhou/

🧑‍🔬 Hang Zhou

🏛️ Affiliations

🧭 Fields, topics and concepts

📅 Works by year

2020s

📄 Papers

🤝 Co-authors here

🔗 Cited by

Hang Zhou

Affiliations

Fields, topics and concepts

Works by year

Papers

Co-authors here

Cited by