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Ying He

Ying He has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning, GPU & parallel computing; often with Chengqi Deng, Chong Ruan, Damai Dai.

2 papers in our library · 2025–2026

OpenAlex · h-index 0 · 3 works · 0 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 33 times in our library · ⭐ Paper Stars ranking: #7269

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

🎓Fudan UniversityShanghai · China2026 · 1 papercurrent🎓Sun Yat-sen UniversityGuangzhou · China2026 · 1 papercurrent

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

 Fields, topics and concepts

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

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArts and Humanities · 1 workArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksHardware and Architecture · 1 workLanguage and Linguistics · 1 work

🧭 Topics 4Research subjects and languages the work is about.GPU & parallel computing · 1 workLanguage models & pretraining · 1 workReasoning · 1 workVision & language · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architectures (2025 · 74 citations)
    Hardware, more than algorithms, now limits how large language models scale. Memory capacity, compute efficiency and interconnect bandwidth all trail model growth, and only a few companies can afford giant clusters. This Industry Track paper from DeepSeek-AI at ISCA '25 does not repeat the DeepSeek-V3 technical…
  2. Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning (2026)
    Test-time scaling (TTS) improves model output by spending extra compute at inference instead of adding parameters or training data.

 Co-authors here

7 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 33 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated

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