| Learning from noisy labels with deep neural networks: A survey H Song, M Kim, D Park, Y Shin, JG Lee IEEE transactions on neural networks and learning systems 34 (11), 8135-8153, 2022 | 1263 | 2022 |
| Selfie: Refurbishing unclean samples for robust deep learning H Song, M Kim, JG Lee International conference on machine learning, 5907-5915, 2019 | 480 | 2019 |
| How does early stopping help generalization against label noise? H Song, M Kim, D Park, JG Lee arXiv preprint arXiv:1911.08059, 2019 | 59 | 2019 |
| Robust learning by self-transition for handling noisy labels H Song, M Kim, D Park, Y Shin, JG Lee Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 44 | 2021 |
| Hi-COVIDNet: Deep learning approach to predict inbound COVID-19 patients and case study in South Korea M Kim, J Kang, D Kim, H Song, H Min, Y Nam, D Park, JG Lee Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 39 | 2020 |
| Prestopping: How does early stopping help generalization against label noise? H Song, M Kim, D Park, JG Lee | 29 | 2019 |
| Meta-learning for online update of recommender systems M Kim, H Song, Y Shin, D Park, K Shin, JG Lee Proceedings of the AAAI Conference on Artificial Intelligence 36 (4), 4065-4074, 2022 | 21 | 2022 |
| Carpe diem, seize the samples uncertain" at the moment" for adaptive batch selection H Song, M Kim, S Kim, JG Lee Proceedings of the 29th ACM International Conference on Information …, 2020 | 21 | 2020 |
| Ada-boundary: accelerating DNN training via adaptive boundary batch selection H Song, S Kim, M Kim, JG Lee Machine Learning 109, 1837-1853, 2020 | 19 | 2020 |
| PREMERE: Meta-Reweighting via Self-Ensembling for Point-of-Interest Recommendation M Kim, H Song, D Kim, K Shin, JG Lee AAAI, 2021 | 16 | 2021 |
| TRAP: Two-level regularized autoencoder-based embedding for power-law distributed data D Park, H Song, M Kim, JG Lee Proceedings of The Web Conference 2020, 1615-1624, 2020 | 11 | 2020 |
| Task-agnostic undesirable feature deactivation using out-of-distribution data D Park, H Song, MS Kim, JG Lee Advances in Neural Information Processing Systems 34, 4040-4052, 2021 | 10 | 2021 |
| Covid-eenet: Predicting fine-grained impact of COVID-19 on local economies D Kim, H Min, Y Nam, H Song, S Yoon, M Kim, JG Lee Proceedings of the AAAI Conference on Artificial Intelligence 36 (11), 11971 …, 2022 | 9 | 2022 |
| Toward robustness in multi-label classification: A data augmentation strategy against imbalance and noise H Song, M Kim, JG Lee Proceedings of the AAAI Conference on Artificial Intelligence 38 (19), 21592 …, 2024 | 7 | 2024 |
| Debiasing neighbor aggregation for graph neural network in recommender systems M Kim, J Oh, J Do, S Lee Proceedings of the 31st ACM International Conference on Information …, 2022 | 7 | 2022 |
| RepAugment: Input-Agnostic Representation-Level Augmentation for Respiratory Sound Classification JW Kim, M Toikkanen, S Bae, M Kim, HY Jung arXiv preprint arXiv:2405.02996, 2024 | 5 | 2024 |
| Aligning Large Language Models via Fine-grained Supervision D Xu, L Qiu, M Kim, F Ladhak, J Do ACL, 2024 | 2 | 2024 |
| Method and apparatus for predicting imported infectious disease information based on deep neural networks J Lee, M Kim, J Kang, D Kim, S Hwanjun, MIN Hyangsuk, NAM Youngeun, ... US Patent 11,557,401, 2023 | 1 | 2023 |
| Method and apparatus for predicting imported infectious disease information based on deep neural networks D Park, Y Nam, H Min, H Song, D Kim, J Kang, MS Kim, JG Lee US, 2022 | | 2022 |
Jae-Gil LeeProfessor, School of Computing, KAISTZweryfikowany adres z kaist.ac.kr