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Ying Lin

Ying Lin has 3 papers in our library, in 2020–2026, most of them on Language models & pretraining, Information extraction, Reasoning; often with Bryan Catanzaro, Mohammad Shoeybi, Mostofa Patwary.

3 papers in our library · 2020–2026

OpenAlex · ORCID · h-index 13 · 46 works · 7,302 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 367 times in our library · ⭐ Paper Stars ranking: #3197

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. 17 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. A Joint Neural Model for Information Extraction with Global Features (2020 · 602 citations)
    Joint neural information extraction systems of the time still labelled each trigger, entity, relation and argument with its own local classifier.
  2. Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset (2025 · 65 citations)
    Leading English web corpora built from Common Crawl, such as FineWeb-Edu and DCLM, raise benchmark scores by discarding roughly nine tenths of the crawl with model-based filters.
  3. Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning (2026 · 9 citations)
    Reinforcement learning (RL) has improved LLM reasoning mainly in math and code, where answers can be checked automatically.

 Co-authors here

12 of 15 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 367 times (of the 149,439 papers whose bibliographies we read); 68 citing papers with a page here →.

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