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Sungchul Kim

20 papers hereh-index 9318 citations26 works total

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

author position
  • middle author19

Across the 19 of 20 papers where every author was matched, so the position is known.

fields
  • cs.CL10
  • cs.LG4
  • cs.HC3
  • cs.CV1
  • cs.IR1
  • eess.IV1
same name
  • Sungchul Kim — 28 papers, h 25
  • Sungchul Kim — 20 papers, h 10
  • Sungchul Kim — 6 papers, h 4
  • Sungchul Kim — 1 paper, h 3
  • Sungchul Kim — 1 paper, h 5

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
20232026
most citedSciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings

11 citations · 20 across the 19 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2024★ 4 cited

Visual Prompting in Multimodal Large Language Models: A Survey

Junda Wu, Zhehao Zhang, Yu Xia +12

Multimodal large language models (MLLMs) equip pre-trained large-language models (LLMs) with visual capabilities. While textual prompting in LLMs has been widely studied, visual pr…

cs.LG2024

Federated Large Language Models: Current Progress and Future Directions

Yuhang Yao, Jianyi Zhang, Junda Wu +11

Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy…

cs.LG2024

Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits

Yu Xia, Fang Kong, Tong Yu +4

Web-based applications such as chatbots, search engines and news recommendations continue to grow in scale and complexity with the recent surge in the adoption of LLMs. Online mode…

cs.LG2023

Leveraging Graph Diffusion Models for Network Refinement Tasks

Puja Trivedi, Ryan Rossi, David Arbour +7

Most real-world networks are noisy and incomplete samples from an unknown target distribution. Refining them by correcting corruptions or inferring unobserved regions typically imp…

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