Jong-June Jeon

About Jong-June Jeon

Jong-June Jeon, With an exceptional h-index of 12 and a recent h-index of 11 (since 2020), a distinguished researcher at University of Seoul, specializes in the field of machine learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Adaptive Adversarial Augmentation for Molecular Property Prediction

Geodesic multi-modal mixup for robust fine-tuning

Distributional learning of variational autoencoder: Application to synthetic data generation

Customization of latent space in semi-supervised Variational AutoEncoder

Balanced Marginal and Joint Distributional Learning via Mixture Cramer-Wold Distance

Uniform pessimistic risk and optimal portfolio

Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms

Causally disentangled generative variational autoencoder

Jong-June Jeon Information

University

Position

___

Citations(all)

575

Citations(since 2020)

453

Cited By

280

hIndex(all)

12

hIndex(since 2020)

11

i10Index(all)

16

i10Index(since 2020)

12

Email

University Profile Page

Google Scholar

Jong-June Jeon Skills & Research Interests

machine learning

Top articles of Jong-June Jeon

Title

Journal

Author(s)

Publication Date

Adaptive Adversarial Augmentation for Molecular Property Prediction

Soyoung Cho

Sungchul Hong

Jong-June Jeon

2024/2/28

Geodesic multi-modal mixup for robust fine-tuning

Advances in Neural Information Processing Systems

Changdae Oh

Junhyuk So

Hoyoon Byun

YongTaek Lim

Minchul Shin

...

2024/2/13

Distributional learning of variational autoencoder: Application to synthetic data generation

Advances in Neural Information Processing Systems

Seunghwan An

Jong-June Jeon

2024/2/13

Customization of latent space in semi-supervised Variational AutoEncoder

Pattern Recognition Letters

Seunghwan An

Jong-June Jeon

2024/1/1

Balanced Marginal and Joint Distributional Learning via Mixture Cramer-Wold Distance

arXiv preprint arXiv:2312.03307

Seunghwan An

Sungchul Hong

Jong-June Jeon

2023/12/6

Uniform pessimistic risk and optimal portfolio

arXiv preprint arXiv:2303.07158

Sungchul Hong

Jong-June Jeon

2023/3/2

Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms

arXiv preprint arXiv:2303.00515

Sunghcul Hong

Yunjin Choi

Jong-June Jeon

2023/2/28

Causally disentangled generative variational autoencoder

arXiv preprint arXiv:2302.11737

SeungHwan An

Kyungwoo Song

Jong-June Jeon

2023/2/23

Interpretable Transformer for Water Level Forecasting

arXiv e-prints

Sunghcul Hong

Yunjin Choi

Jong-June Jeon

2023/2

Clustering for Regional Time Trend in the Nonstationary Extreme Distribution

Water

Sungchul Hong

Jong-June Jeon

Yongdai Kim

2022/1

Learning a high-dimensional linear structural equation model via l1-regularized regression

Journal of Machine Learning Research

Gunwoong Park

Sang Jun Moon

Sion Park

Jong-June Jeon

2021

Learning multiple quantiles with neural networks

Journal of Computational and Graphical Statistics

Sang Jun Moon

Jong-June Jeon

Jason Sang Hun Lee

Yongdai Kim

2021/10/2

Statistical Road-Traffic Noise Mapping Based on Elementary Urban Forms in Two Cities of South Korea

Sustainability

Phillip Kim

Hunjae Ryu

Jong-June Jeon

Seo Il Chang

2021/1

Can patient triaging with clinical scoring systems reduce CT use in adolescents and young adults suspected of having appendicitis?

Radiology

Hyunjoo Song

Seungjae Lee

Ji Hoon Park

Hae Young Kim

Hooney Daniel Min

...

2021/8

Exon: Explainable encoder network

arXiv preprint arXiv:2105.10867

SeungHwan An

Hosik Choi

Jong-June Jeon

2021/5/23

Differentiation between complicated and uncomplicated appendicitis: diagnostic model development and validation study

Abdominal Radiology

Hae Young Kim

Ji Hoon Park

Sung Soo Lee

Jong-June Jeon

Chang Jin Yoon

...

2021/3

Regularized within-class precision matrix based PLDA in text-dependent speaker verification

Applied Sciences

Sung-Hyun Yoon

Jong-June Jeon

Ha-Jin Yu

2020/9/20

Assessment of inter-model variability in meteorological drought characteristics using CMIP5 GCMs over South Korea

KSCE Journal of Civil Engineering

Jang Hyun Sung

Junehyeong Park

Jong-June Jeon

Seung Beom Seo

2020/9

Primal path algorithm for compositional data analysis

Computational Statistics & Data Analysis

Jong-June Jeon

Yongdai Kim

Sungho Won

Hosik Choi

2020/8/1

See List of Professors in Jong-June Jeon University(University of Seoul)

Co-Authors

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