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Dongjun Kim

13 papers hereh-index 111.1k citations26 works total

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

author position
  • first author8
  • middle author4

Across the 12 of 13 papers where every author was matched, so the position is known.

fields
  • cs.LG9
  • cs.AI1
  • cs.CV1
  • cs.MA1
  • stat.ME1
same name
  • Dongjun Kim — 10 papers, h 6
  • Dongjun Kim — 9 papers, h 2
  • Dongjun Kim — 2 papers, h 1
  • Dongjun Kim — 1 paper, h 3
  • Dongjun Kim — 1 paper, h 2
  • Dongjun Kim — 1 paper, h 1

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
20192024
most citedConsistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion

16 citations · 61 across the 13 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

stat.ME2020

Sequential Likelihood-Free Inference with Neural Proposal

Dongjun Kim, Kyungwoo Song, YoonYeong Kim +4

Bayesian inference without the likelihood evaluation, or likelihood-free inference, has been a key research topic in simulation studies for gaining quantitatively validated simulat…

cs.LG2020★ 1 cited

Implicit Kernel Attention

Kyungwoo Song, Yohan Jung, Dongjun Kim +1

\textit{Attention} computes the dependency between representations, and it encourages the model to focus on the important selective features. Attention-based models, such as Transf…

cs.LG2020★ 2 cited

Adversarial Likelihood-Free Inference on Black-Box Generator

Dongjun Kim, Weonyoung Joo, Seungjae Shin +2

Generative Adversarial Network (GAN) can be viewed as an implicit estimator of a data distribution, and this perspective motivates using the adversarial concept in the true input p…

cs.LG2020★ 8 cited

Generalized Gumbel-Softmax Gradient Estimator for Generic Discrete Random Variables

Weonyoung Joo, Dongjun Kim, Seungjae Shin +1

Estimating the gradients of stochastic nodes in stochastic computational graphs is one of the crucial research questions in the deep generative modeling community, which enables th…

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