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Noseong Park

46 papers hereh-index 313.4k citations91 works total

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

author position
  • first author2
  • middle author15
  • last author28

Across the 45 of 46 papers where every author was matched, so the position is known.

fields
  • cs.LG28
  • cs.SI5
  • cs.IR4
  • cs.CR3
  • cs.CV2
  • cs.AI1
same name
  • Noseong Park — 40 papers, h 10
  • Noseong Park — 4 papers, h 8
  • Noseong Park — 4 papers, h 2
  • Noseong Park — 1 paper

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
20162026
most citedData Synthesis based on Generative Adversarial Networks

550 citations · 718 across the 45 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.SI2018

GI-OHMS: Graphical Inference to Detect Overlapping Communities

Nasheen Nur, Wenwen Dou, Xi Niu +2

Discovery of communities in complex networks is a topic of considerable recent interest within the complex systems community. Due to the dynamic and rapidly evolving nature of larg…

cs.SI2018

Ensemble-based Overlapping Community Detection using Disjoint Community Structures

Tanmoy Chakraborty, Saptarshi Ghosh, Noseong Park

While there has been a plethora of approaches for detecting disjoint communities from real-world complex networks, some methods for detecting overlapping community structures have…

cs.DB2018★ 550 cited

Data Synthesis based on Generative Adversarial Networks

Noseong Park, Mahmoud Mohammadi, Kshitij Gorde +3

Privacy is an important concern for our society where sharing data with partners or releasing data to the public is a frequent occurrence. Some of the techniques that are being use…

cs.CV2018

MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation

David Keetae Park, Seungjoo Yoo, Hyojin Bahng +2

Recently, generative adversarial networks (GANs) have shown promising performance in generating realistic images. However, they often struggle in learning complex underlying modali…

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