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Xingcheng Yao

Xingcheng Yao has 2 papers in our library, in 2021–2025, most of them on Efficient models & compression; often with Danqi Chen, Enzhe Lu, Jiaxi Hu.

2 papers in our library · 2021–2025

OpenAlex · ORCID · Wikidata · h-index 5 · 13 works · 2,749 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1,809 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #863

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

🎓University of California, Los AngelesLos Angeles · United States2025 · 3 paperscurrent🎓Tsinghua UniversityBeijing · China2021–2023 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Efficient models & compression · 1 work

💡 Concepts 2Key ideas and methods explained on our concept pages.Natural language processing · 1 workSemantic similarity · 1 work

 Works by year

2020s

20251 paper20211 paper

 Papers

The most cited first (OpenAlex and our library).

  1. SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021 · 3,942 citations)
    Pre-trained encoders such as BERT and RoBERTa produce poor general-purpose sentence vectors without further training. Their raw embedding space is anisotropic, and text is hard to augment because words are discrete. The authors introduce SimCSE, a contrastive framework with two variants.
  2. Kimi Linear: An Expressive, Efficient Attention Architecture (2025 · 51 citations)
    Softmax attention becomes a bottleneck for agentic and RL-heavy LLM workloads. Its cost grows quadratically with sequence length, and its KV cache keeps expanding during long decoding. Earlier linear-attention and hybrid designs saved compute but usually lost quality or were only tested at small scale. The Kimi Team…

All works

4 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. SimCSE: Simple Contrastive Learning of Sentence EmbeddingsConference on Empirical Methods in Natural Language Processing (EMNLP)Tianyu Gao, Xingcheng Yao, Danqi Chen2021 · 3,942 citations
  2. Muon is Scalable for LLM Training ↗arXiv:2502.16982 · no summary yetJingyuan Liu et al.2025 · 83 citations
  3. Kimi Linear: An Expressive, Efficient Attention ArchitecturearXiv:2510.26692Kimi Team, Yu Zhang, Zongyu Lin, et al.2025 · 51 citations
  4. NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework ↗arXiv (Cornell University) · no summary yetXingcheng Yao et al.2021 · 34 citations

 Co-authors here

8 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 1,809 times (of the 149,439 papers whose bibliographies we read); 325 citing papers with a page here →.

Updated

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