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ORCIDYongdai Kim
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Publications
- 2024
[c13]Dongha Kim, Yongchan Choi, Kunwoong Kim, Ilsang Ohn, Yongdai Kim:
IOFM: Using the Interpolation Technique on the Over-Fitted Models to Identify Clean-Annotated Samples. AAAI 2024: 13104-13112- [c12]Dongha Kim, Jaesung Hwang, Jongjin Lee, Kunwoong Kim, Yongdai Kim:
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models. ICML 2024 - 2023
- [j28]Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin:
A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models. J. Mach. Learn. Res. 24: 77:1-77:42 (2023) - [i19]Dongha Kim, Jaesung Hwang, Jongjin Lee, Kunwoong Kim, Yongdai Kim:
ODIM: an efficient method to detect outliers via inlier-memorization effect of deep generative models. CoRR abs/2301.04257 (2023) - 2022
- [c7]Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim:
Learning fair representation with a parametric integral probability metric. ICML 2022: 11074-11101 - [i12]Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim:
Learning fair representation with a parametric integral probability metric. CoRR abs/2202.02943 (2022) - 2021
- [j23]Dongha Kim, Yongdai Kim
:
Understanding Effects of Architecture Design to Invariance and Complexity in Deep Neural Networks. IEEE Access 9: 9670-9681 (2021) - [j21]Yongdai Kim, Ilsang Ohn, Dongha Kim:
Fast convergence rates of deep neural networks for classification. Neural Networks 138: 179-197 (2021) - [c6]Minjin Kim, Young-geun Kim, Dongha Kim, Yongdai Kim, Myunghee Cho Paik:
Kernel-convoluted Deep Neural Networks with Data Augmentation. AAAI 2021: 8155-8162 - [i9]Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin:
A likelihood approach to nonparametric estimation of a singular distribution using deep generative models. CoRR abs/2105.04046 (2021) - [i8]Dongha Kim, Yongchan Choi, Kunwoong Kim, Yongdai Kim:
INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks. CoRR abs/2106.15185 (2021) - 2020
- [c5]Dongha Kim, Jaesung Hwang, Yongdai Kim:
On casting importance weighted autoencoder to an EM algorithm to learn deep generative models. AISTATS 2020: 2153-2163 - [i6]Minjin Kim, Young-geun Kim, Dongha Kim, Yongdai Kim, Myunghee Cho Paik:
Kernel-convoluted Deep Neural Networks with Data Augmentation. CoRR abs/2012.02521 (2020) - 2019
- [j18]Dongha Kim, JongRoul Woo, Jungwoo Shin, Jongsu Lee, Yongdai Kim:
Can search engine data improve accuracy of demand forecasting for new products? Evidence from automotive market. Ind. Manag. Data Syst. 119(5): 1089-1103 (2019) - [i4]Dongha Kim, Yongchan Choi, Yongdai Kim:
Understanding and Improving Virtual Adversarial Training. CoRR abs/1909.06737 (2019) - 2018
- [i3]Yongdai Kim, Dongha Kim:
On variation of gradients of deep neural networks. CoRR abs/1812.00308 (2018) - [i2]Yongdai Kim, Ilsang Ohn, Dongha Kim:
Fast convergence rates of deep neural networks for classification. CoRR abs/1812.03599 (2018)
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