Elamur Research⌕
☰
home / authors / Ke Chen

Ke Chen

Ke Chen has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining, Vision & language, Efficient models & compression; often with Huan Li, Jun Zhang, Lidan Shou.

2 papers in our library · 2024–2026

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

📄 2 works in our library · 🔗 Cited 112 times in our library · ⭐ Paper Stars ranking: #6174

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

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

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksEfficient models & compression · 1 workVision & language · 1 work

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding (2024 · 148 citations)
    Autoregressive generation in large language models is slow. Each new token needs a full, memory-bandwidth-bound pass through the network. Speculative decoding can help, but it usually depends on a separate draft model, which costs extra GPU memory and is hard to obtain for fine-tuned models such as LLaMA-2-Chat or…
  2. HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference (2026 · 4 citations)
    Multimodal LLMs turn every image or video frame into hundreds or thousands of tokens. As a result, the key–value (KV) cache held in GPU memory during decoding grows quickly and slows inference. Earlier compression methods only decide how much cache each token, layer or head keeps, and they apply a single strategy…

 Co-authors here

5 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 112 times (of the 149,439 papers whose bibliographies we read); 19 citing papers with a page here →.

Updated

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