| Refine myself by teaching myself: Feature refinement via self-knowledge distillation M Ji, S Shin, S Hwang, G Park, IC Moon Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 228 | 2021 |
| Adversarial dropout for supervised and semi-supervised learning S Park, JK Park, SJ Shin, IC Moon Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 221 | 2018 |
| Mining social networks for personalized email prioritization S Yoo, Y Yang, F Lin, IC Moon Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009 | 152 | 2009 |
| Analysis of twitter lists as a potential source for discovering latent characteristics of users K Dongwoo, J Yohan, M Il-Chul, AH Oh ACM SIGCHI'10 Workshop on Microblogging, 2010 | 146 | 2010 |
| Dirichlet variational autoencoder W Joo, W Lee, S Park, IC Moon Pattern Recognition 107, 107514, 2020 | 137 | 2020 |
| Refining generative process with discriminator guidance in score-based diffusion models D Kim, Y Kim, SJ Kwon, W Kang, IC Moon arXiv preprint arXiv:2211.17091, 2022 | 135 | 2022 |
| Modeling and simulating terrorist networks in social and geospatial dimensions IC Moon, KM Carley IEEE Intelligent Systems 22 (5), 40-49, 2007 | 134 | 2007 |
| Soft truncation: A universal training technique of score-based diffusion model for high precision score estimation D Kim, S Shin, K Song, W Kang, IC Moon arXiv preprint arXiv:2106.05527, 2021 | 111 | 2021 |
| Efficient extraction of domain specific sentiment lexicon with active learning S Park, W Lee, IC Moon Pattern Recognition Letters 56, 38-44, 2015 | 89 | 2015 |
| Counterfactual fairness with disentangled causal effect variational autoencoder H Kim, S Shin, JH Jang, K Song, W Joo, W Kang, IC Moon Proceedings of the AAAI Conference on Artificial Intelligence 35 (9), 8128-8136, 2021 | 84 | 2021 |
| Augmented variational autoencoders for collaborative filtering with auxiliary information W Lee, K Song, IC Moon Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017 | 78 | 2017 |
| ORA User's Guide 2008 KM Carley, D Columbus, M DeReno, J Reminga, IC Moon | 77 | 2008 |
| Maximum likelihood training of implicit nonlinear diffusion model D Kim, B Na, SJ Kwon, D Lee, W Kang, IC Moon Advances in neural information processing systems 35, 32270-32284, 2022 | 71 | 2022 |
| Unknown-aware domain adversarial learning for open-set domain adaptation JH Jang, B Na, DH Shin, M Ji, K Song, IC Moon Advances in Neural Information Processing Systems 35, 16755-16767, 2022 | 63 | 2022 |
| Lada: Look-ahead data acquisition via augmentation for deep active learning YY Kim, K Song, JH Jang, IC Moon Advances in Neural Information Processing Systems 34, 22919-22930, 2021 | 48 | 2021 |
| Diagnosis prediction via medical context attention networks using deep generative modeling W Lee, S Park, W Joo, IC Moon 2018 IEEE International Conference on Data Mining (ICDM), 1104-1109, 2018 | 45 | 2018 |
| Loss-curvature matching for dataset selection and condensation S Shin, H Bae, D Shin, W Joo, IC Moon International Conference on Artificial Intelligence and Statistics, 8606-8628, 2023 | 40 | 2023 |
| Sequential recommendation with relation-aware kernelized self-attention M Ji, W Joo, K Song, YY Kim, IC Moon Proceedings of the AAAI conference on artificial intelligence 34 (04), 4304-4311, 2020 | 40 | 2020 |
| Hierarchical context enabled recurrent neural network for recommendation K Song, M Ji, S Park, IC Moon Proceedings of the AAAI conference on artificial intelligence 33 (01), 4983-4991, 2019 | 39 | 2019 |
| From noisy prediction to true label: Noisy prediction calibration via generative model HS Bae, S Shin, B Na, JH Jang, K Song, IC Moon International Conference on Machine Learning, 1277-1297, 2022 | 38 | 2022 |
Kyungwoo SongYonsei UniversityVerified email at yonsei.ac.kr
