| Generative adversarial trainer: Defense to adversarial perturbations with gan H Lee, S Han, J Lee arXiv preprint arXiv:1705.03387, 2017 | 179 | 2017 |
| On the convergence of continual learning with adaptive methods S Han, Y Kim, T Cho, J Lee Uncertainty in Artificial Intelligence, 809-818, 2023 | 1 | 2023 |
| SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning D Lee, S Han, T Cho, J Lee Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion T Cho, S Han, H Lee, K Lee, J Lee Thirty-seventh Conference on Neural Information Processing Systems, 2023 | | 2023 |
| Perturbed Quantile Regression for Distributional Reinforcement Learning TH Cho, S Han, H Lee, K Lee, J Lee Deep Reinforcement Learning Workshop NeurIPS 2022, 2022 | | 2022 |
| Adaptive Methods for Nonconvex Continual Learning S Han, Y Kim, TH Cho, J Lee OPT 2022: Optimization for Machine Learning (NeurIPS 2022 Workshop), 2022 | | 2022 |
| Learning to Learn Unlearned Feature for Brain Tumor Segmentation H Sungyeob, K Yeongmo, H Seokhyeon, L Jungwoo, S Choi Medical Imaging meets NeurIPS, 2018 | | 2018 |
| Unsupervised classification into unknown number of classes S Han, D Kim, J Lee | | 2018 |
Jungwoo LeeProfessor, Department of Electrical and Computer Engineering, Seoul National UniversityVerified email at snu.ac.kr
