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researcher

Min Ma

12 papers hereh-index 74.8k citations16 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author6

Across the 8 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CL8
  • cs.CV3
  • cs.LG1
same name
  • Min Ma — 7 papers, h 9
  • Min Ma — 4 papers, h 1
  • Min Ma — 2 papers, h 10
  • Min Ma — 1 paper, h 0
  • Min Ma — 1 paper, h 8
  • Min Ma — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedGemma 3 Technical Report

65 citations · 69 across the 12 of their papers we have counts for

collaborators
Showing 2025 · cs.CLShow all

4 papers · 2 filters

cs.CL2025

Encoder-Decoder or Decoder-Only? Revisiting Encoder-Decoder Large Language Model

Biao Zhang, Yong Cheng, Siamak Shakeri +3

Recent large language model (LLM) research has undergone an architectural shift from encoder-decoder modeling to nowadays the dominant decoder-only modeling. This rapid transition,…

cs.CL2025

EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models

Zekun Wang, Minghua Ma, Zexin Wang +4

Large Vision-Language Models (LVLMs) have achieved remarkable success, yet their significant computational demands hinder practical deployment. While efforts to improve LVLM effici…

cs.CL2025

Encoder-Decoder Gemma: Improving the Quality-Efficiency Trade-Off via Adaptation

Biao Zhang, Fedor Moiseev, Joshua Ainslie +7

While decoder-only large language models (LLMs) have shown impressive results, encoder-decoder models are still widely adopted in real-world applications for their inference effici…

cs.CL2025★ 65 cited

Gemma 3 Technical Report

Gemma Team, Aishwarya Kamath, Johan Ferret +209

We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision underst…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.