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, 2022 | 918 | 2022 |
Selfie: Refurbishing unclean samples for robust deep learning H Song, M Kim, JG Lee International conference on machine learning, 5907-5915, 2019 | 381 | 2019 |
How does early stopping help generalization against label noise? H Song, M Kim, D Park, JG Lee arXiv preprint arXiv:1911.08059, 2019 | 40 | 2019 |
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 | 38 | 2020 |
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 | 36 | 2021 |
Prestopping: How does early stopping help generalization against label noise? H Song, M Kim, D Park, JG Lee | 33 | 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 | 16 | 2022 |
Ada-boundary: accelerating DNN training via adaptive boundary batch selection H Song, S Kim, M Kim, JG Lee Machine Learning 109, 1837-1853, 2020 | 16 | 2020 |
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 | 15 | 2020 |
PREMERE: Meta-Reweighting via Self-Ensembling for Point-of-Interest Recommendation M Kim, H Song, D Kim, K Shin, JG Lee AAAI, 2021 | 14 | 2021 |
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 |
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 | 10 | 2020 |
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 | 8 | 2022 |
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 | 6 | 2022 |
Aligning Large Language Models via Fine-grained Supervision D Xu, L Qiu, M Kim, F Ladhak, J Do ACL, 2024 | | 2024 |
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 | | 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 | | 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 |