| Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks Y Kim, T Moon IEEE geoscience and remote sensing letters 13 (1), 8-12, 2015 | 666 | 2015 |
| An unbiased offline evaluation of contextual bandit algorithms with generalized linear models L Li, W Chu, J Langford, T Moon, X Wang Proceedings of the Workshop on On-line Trading of Exploration and …, 2012 | 264 | 2012 |
| Fooling neural network interpretations via adversarial model manipulation J Heo, S Joo, T Moon Advances in neural information processing systems 32, 2019 | 223 | 2019 |
| Uncertainty-based continual learning with adaptive regularization H Ahn, S Cha, D Lee, T Moon Advances in neural information processing systems 32, 2019 | 204 | 2019 |
| Ss-il: Separated softmax for incremental learning H Ahn, J Kwak, S Lim, H Bang, H Kim, T Moon Proceedings of the IEEE/CVF International conference on computer vision, 844-853, 2021 | 192 | 2021 |
| Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation TD Bui, J Shin, T Moon Biomedical Signal Processing and Control 54, 101613, 2019 | 165* | 2019 |
| Rnndrop: A novel dropout for rnns in asr T Moon, H Choi, H Lee, I Song 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU …, 2015 | 141 | 2015 |
| Micro-Doppler based classification of human aquatic activities via transfer learning of convolutional neural networks J Park, RJ Javier, T Moon, Y Kim Sensors 16 (12), 1990, 2016 | 135 | 2016 |
| Learning to model relatedness for news recommendation Y Lv, T Moon, P Kolari, Z Zheng, X Wang, Y Chang Proceedings of the 20th international conference on World wide web, 57-66, 2011 | 133 | 2011 |
| Toward a unified framework for interpreting machine-learning models in neuroimaging L Kohoutová, J Heo, S Cha, S Lee, T Moon, TD Wager, CW Woo Nature protocols 15 (4), 1399-1435, 2020 | 127 | 2020 |
| Subtask gated networks for non-intrusive load monitoring C Shin, S Joo, J Yim, H Lee, T Moon, W Rhee Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 1150-1157, 2019 | 119 | 2019 |
| Continual learning with node-importance based adaptive group sparse regularization S Jung, H Ahn, S Cha, T Moon Advances in neural information processing systems 33, 3647-3658, 2020 | 111 | 2020 |
| Estimating PM2. 5 concentration of the conterminous United States via interpretable convolutional neural networks Y Park, B Kwon, J Heo, X Hu, Y Liu, T Moon Environmental Pollution 256, 113395, 2020 | 108 | 2020 |
| An online learning framework for refining recency search results with user click feedback T Moon, W Chu, L Li, Z Zheng, Y Chang ACM Transactions on Information Systems (TOIS) 30 (4), 1-28, 2012 | 85* | 2012 |
| Ssul: Semantic segmentation with unknown label for exemplar-based class-incremental learning S Cha, YJ Yoo, T Moon Advances in neural information processing systems 34, 10919-10930, 2021 | 73 | 2021 |
| DUDE-Seq: fast, flexible, and robust denoising for targeted amplicon sequencing B Lee, T Moon, S Yoon, T Weissman PloS one 12 (7), e0181463, 2017 | 71 | 2017 |
| CPR: classifier-projection regularization for continual learning S Cha, H Hsu, T Hwang, FP Calmon, T Moon arXiv preprint arXiv:2006.07326, 2020 | 70 | 2020 |
| Fair feature distillation for visual recognition S Jung, D Lee, T Park, T Moon Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 66 | 2021 |
| xxAI-beyond explainable AI: international workshop, held in conjunction with ICML 2020, July 18, 2020, Vienna, Austria, revised and extended papers A Holzinger, R Goebel, R Fong, T Moon, KR Müller, W Samek Springer Nature, 2022 | 57* | 2022 |
| Fully convolutional pixel adaptive image denoiser S Cha, T Moon Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 56 | 2019 |
Sungmin Cha (차성민)Faculty Fellow, New York Universitynyu.edu의 이메일 확인됨
