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Woojun Kim

KAIST

4 papers hereh-index 242.8k citations197 works total

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

author position
  • first author2
  • middle author1

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cond-mat.mes-hall1
  • cs.LG1
  • eess.IV1
  • stat.ME1
affiliations
  • KAIST
ORCID 0000-0001-8886-8848
same name
  • Woojun Kim — 16 papers, h 4
  • Woojun Kim — 10 papers, h 8
  • Woojun Kim — 1 paper
  • Woojun Kim — 1 paper
  • Woojun Kim — 1 paper, h 3

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

most citedFederated Semi-Supervised Learning with Prototypical Networks

5 citations · 6 across the 4 of their papers we have counts for

collaborators

4 papers

stat.ME2023

Peak-Persistence Diagrams for Estimating Shapes and Functions from Noisy Data

Woo Min Kim, Sutanoy Dasgupta, Anuj Srivastava

Estimating signals underlying noisy data is a significant problem in statistics and engineering. Numerous estimators are available in the literature, depending on the observation m…

cond-mat.mes-hall2022★ 1 cited

Interaction of gapless spin waves and a domain wall in an easy-cone ferromagnet

Wooyon Kim, Se Kwon Kim

We theoretically study the interaction of spin waves and a domain wall in a quasi-one-dimensional easy-cone ferromagnet. The gapless spin waves on top of a domain wall are found to…

eess.IV2022

Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography

Alberto Gomez, Mihaela Porumb, Angela Mumith +5

We propose a new method to automatically contour the left ventricle on 2D echocardiographic images. Unlike most existing segmentation methods, which are based on predicting segment…

cs.LG2022★ 5 cited

Federated Semi-Supervised Learning with Prototypical Networks

Woojung Kim, Keondo Park, Kihyuk Sohn +2

With the increasing computing power of edge devices, Federated Learning (FL) emerges to enable model training without privacy concerns. The majority of existing studies assume the…

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