| Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders JY Kim, SJ Bu, SB Cho Information Sciences 460, 83-102, 2018 | 242 | 2018 |
| Electric energy consumption prediction by deep learning with state explainable autoencoder JY Kim, SB Cho Energies 12 (4), 739, 2019 | 129 | 2019 |
| Malware detection using deep transferred generative adversarial networks JY Kim, SJ Bu, SB Cho Neural Information Processing: 24th International Conference, ICONIP 2017 …, 2017 | 95 | 2017 |
| Obfuscated malware detection using deep generative model based on global/local features JY Kim, SB Cho Computers & Security 112, 102501, 2022 | 45 | 2022 |
| Evolutionary Optimization of Hyperparameters in Deep Learning Models JY Kim, SB Cho 2019 IEEE Congress on Evolutionary Computation (CEC), 831-837, 2019 | 30 | 2019 |
| Explainable prediction of electric energy demand using a deep autoencoder with interpretable latent space JY Kim, SB Cho Expert Systems with Applications 186, 115842, 2021 | 27 | 2021 |
| Detecting intrusive malware with a hybrid generative deep learning model JY Kim, SB Cho Intelligent Data Engineering and Automated Learning–IDEAL 2018: 19th …, 2018 | 22 | 2018 |
| Fair representation for safe artificial intelligence via adversarial learning of unbiased information bottleneck. JY Kim, SB Cho SafeAI@ AAAI, 105-112, 2020 | 17 | 2020 |
| An information theoretic approach to reducing algorithmic bias for machine learning JY Kim, SB Cho Neurocomputing 500, 26-38, 2022 | 16 | 2022 |
| A systematic analysis and guidelines of graph neural networks for practical applications JY Kim, SB Cho Expert Systems with Applications 184, 115466, 2021 | 14 | 2021 |
| Deep CNN transferred from VAE and GAN for classifying irritating noise in automobile JY Kim, SB Cho Neurocomputing 452, 395-403, 2021 | 14 | 2021 |
| Hybrid deep learning based on GAN for classifying BSR noises from invehicle sensors JY Kim, SJ Bu, SB Cho Hybrid Artificial Intelligent Systems: 13th International Conference, HAIS …, 2018 | 14 | 2018 |
| Interpretable deep learning with hybrid autoencoders to predict electric energy consumption JY Kim, SB Cho 15th International Conference on Soft Computing Models in Industrial and …, 2021 | 12 | 2021 |
| A deep neural network ensemble of multimodal signals for classifying excavator operations JY Kim, SB Cho Neurocomputing 470, 290-299, 2022 | 8 | 2022 |
| Addressing negative transfer in diffusion models H Go*, JY Kim*, Y Lee*, S Lee*, S Oh, H Moon, S Choi Advances in Neural Information Processing Systems 36, 2024 | 7 | 2024 |
| Multi-Architecture Multi-Expert Diffusion Models Y Lee*, JY Kim*, H Go*, M Jeong, S Oh, S Choi Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 2023 | 7 | 2023 |
| Towards Practical Plug-and-Play Diffusion Models H Go*, Y Lee*, JY Kim*, S Lee, M Jeong, HS Lee, S Choi IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 | 7 | 2022 |
| Electric energy demand forecasting with explainable time-series modeling JY Kim, SB Cho 2020 International Conference on Data Mining Workshops (ICDMW), 711-716, 2020 | 6 | 2020 |
| Predicting residential energy consumption by explainable deep learning with long-term and short-term latent variables JY Kim, SB Cho Cybernetics and Systems 54 (3), 270-285, 2023 | 5 | 2023 |
| Unsupervised novelty detection in video with adversarial autoencoder based on non-euclidean space JY Kim, SB Cho 2019 15th International Conference on Signal-Image Technology & Internet …, 2019 | 5 | 2019 |
