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Pengchuan Zhang

12 papers hereh-index 916.7k citations46 works total

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

author position
  • middle author3

Across the 3 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CL8
  • cs.CV2
  • cs.AI1
  • cs.LG1
same name
  • Pengchuan Zhang — 26 papers, h 27
  • Pengchuan Zhang — 5 papers, h 5
  • Pengchuan Zhang — 5 papers, h 6
  • Pengchuan Zhang — 5 papers, h 4
  • Pengchuan Zhang — 4 papers, h 4
  • Pengchuan Zhang — 4 papers, h 7

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 citedSAM 3: Segment Anything with Concepts

13 citations · 15 across the 11 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CL2024

LLäMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch

Jan Pfister, Julia Wunderle, Andreas Hotho

We create two German-only decoder models, LLäMmlein 120M and 1B, transparently from scratch and publish them, along with the training data, for the German NLP research community to…

cs.LG2024

TLDR: Token-Level Detective Reward Model for Large Vision Language Models

Deqing Fu, Tong Xiao, Rui Wang +5

Although reward models have been successful in improving multimodal large language models, the reward models themselves remain brutal and contain minimal information. Notably, exis…

cs.AI2024

The Llama 3 Herd of Models

Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…

cs.CV2024★ 1 cited

BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation

Yunhao Ge, Yihe Tang, Jiashu Xu +20

The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels, which real-wo…

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