Haedong Jeong

Haedong Jeong

KAIST

H-index: 5

Asia-South Korea

About Haedong Jeong

Haedong Jeong, With an exceptional h-index of 5 and a recent h-index of 5 (since 2020), a distinguished researcher at KAIST, specializes in the field of Explainable Artificial Intelligence (XAI), Machine Learning, Deep Generative Neural Network.

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

Beyond Single Path Integrated Gradients for Reliable Input Attribution via Randomized Path Sampling

Distilled gradient aggregation: Purify features for input attribution in the deep neural network

On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network

An unsupervised way to understand artifact generating internal units in generative neural networks

Example-based Methods to Explain the Internal Generative Mechanism of Deep Generative Neural Networks

Empirical Study of the Decision Region and Robustness in Deep Neural Networks

of KIISE

Automatic correction of internal units in generative neural networks

Haedong Jeong Information

University

Position

UNIST

Citations(all)

168

Citations(since 2020)

152

Cited By

78

hIndex(all)

5

hIndex(since 2020)

5

i10Index(all)

2

i10Index(since 2020)

2

Email

University Profile Page

Google Scholar

Haedong Jeong Skills & Research Interests

Explainable Artificial Intelligence (XAI)

Machine Learning

Deep Generative Neural Network

Top articles of Haedong Jeong

Beyond Single Path Integrated Gradients for Reliable Input Attribution via Randomized Path Sampling

2023

Distilled gradient aggregation: Purify features for input attribution in the deep neural network

Advances in Neural Information Processing Systems

2022/12/6

On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network

arXiv preprint arXiv:2207.03400

2022/7/7

An unsupervised way to understand artifact generating internal units in generative neural networks

Proceedings of the AAAI Conference on Artificial Intelligence

2022/6/28

Example-based Methods to Explain the Internal Generative Mechanism of Deep Generative Neural Networks

2022

Haedong Jeong
Haedong Jeong

H-Index: 4

Empirical Study of the Decision Region and Robustness in Deep Neural Networks

2021/10/6

Automatic correction of internal units in generative neural networks

2021

An efficient explorative sampling considering the generative boundaries of deep generative neural networks

Proceedings of the AAAI Conference on Artificial Intelligence

2020/4/3

See List of Professors in Haedong Jeong University(KAIST)

Co-Authors

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