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Sungwon Kim

9 papers hereh-index 7138 citations12 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 9 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cond-mat.mtrl-sci1
  • cs.RO1
same name
  • Sungwon Kim — 13 papers, h 11
  • Sungwon Kim — 6 papers, h 9
  • Sungwon Kim — 6 papers, h 4
  • Sungwon Kim — 4 papers
  • Sungwon Kim — 2 papers, h 3
  • Sungwon Kim — 1 paper, h 8

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 citedDSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning

10 citations · 31 across the 7 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cond-mat.mtrl-sci2023★ 3 cited

Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer

Namkyeong Lee, Heewoong Noh, Sungwon Kim +3

The density of states (DOS) is a spectral property of crystalline materials, which provides fundamental insights into various characteristics of the materials. While previous works…

cs.LG2023★ 8 cited

Interpretable Prototype-based Graph Information Bottleneck

Sangwoo Seo, Sungwon Kim, Chanyoung Park

The success of Graph Neural Networks (GNNs) has led to a need for understanding their decision-making process and providing explanations for their predictions, which has given rise…

cs.LG2023★ 10 cited

Task-Equivariant Graph Few-shot Learning

Sungwon Kim, Junseok Lee, Namkyeong Lee +3

Although Graph Neural Networks (GNNs) have been successful in node classification tasks, their performance heavily relies on the availability of a sufficient number of labeled node…

cs.LG2023

Unsupervised Episode Generation for Graph Meta-learning

Jihyeong Jung, Sangwoo Seo, Sungwon Kim +1

We propose Unsupervised Episode Generation method called Neighbors as Queries (NaQ) to solve the Few-Shot Node-Classification (FSNC) task by unsupervised Graph Meta-learning. Doing…

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