| 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 | 205 | 2018 |
| 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 | 160 | 2021 |
| 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 | 154 | 2009 |
| Analysis of twitter lists as a potential source for discovering latent characteristics of users D Kim, Y Jo, IC Moon, A Oh ACM CHI workshop on microblogging 6, 2010 | 147 | 2010 |
| Modeling and simulating terrorist networks in social and geospatial dimensions IC Moon, KM Carley IEEE Intelligent Systems 22 (5), 40-49, 2007 | 130 | 2007 |
| Dirichlet variational autoencoder W Joo, W Lee, S Park, IC Moon Pattern Recognition 107, 107514, 2020 | 109 | 2020 |
| Efficient extraction of domain specific sentiment lexicon with active learning S Park, W Lee, IC Moon Pattern Recognition Letters 56, 38-44, 2015 | 88 | 2015 |
| 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 | 82 | 2022 |
| ORA User's Guide 2008 KM Carley, D Columbus, M DeReno, J Reminga, I Moon Institute for Software Research, School of Computer Science. Pittsburgh …, 2009 | 79 | 2009 |
| 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 | 76 | 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 | 68 | 2017 |
| 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 | 60 | 2021 |
| 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 | 46 | 2022 |
| 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 | 40 | 2018 |
| 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 | 38 | 2020 |
| 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 | 37 | 2021 |
| Are we treating networks seriously? The growth of network research in public administration & public policy S Hwang, IC Moon Connections 29 (2), 4-17, 2009 | 37 | 2009 |
| 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 | 36 | 2019 |
| Personalized email prioritization based on content and social network analysis Y Yang, S Yoo, F Lin, IC Moon IEEE Intelligent Systems 25 (04), 12-18, 2010 | 36 | 2010 |
| Identifying prescription patterns with a topic model of diseases and medications S Park, D Choi, M Kim, W Cha, C Kim, IC Moon Journal of biomedical informatics 75, 35-47, 2017 | 34 | 2017 |
Kyungwoo SongYonsei Universityyonsei.ac.kr의 이메일 확인됨
