| A simple unified framework for detecting out-of-distribution samples and adversarial attacks K Lee, K Lee, H Lee, J Shin Advances in neural information processing systems 31, 2018 | 869 | 2018 |
| Training confidence-calibrated classifiers for detecting out-of-distribution samples K Lee, H Lee, K Lee, J Shin International Conference on Learning Representations, 2017 | 566 | 2017 |
| Using pre-training can improve model robustness and uncertainty D Hendrycks, K Lee, M Mazeika International Conference on Machine Learning, 2712-2721, 2019 | 384 | 2019 |
| Reinforcement learning with augmented data M Laskin, K Lee, A Stooke, L Pinto, P Abbeel, A Srinivas Advances in neural information processing systems, 2020 | 265 | 2020 |
| Decision transformer: Reinforcement learning via sequence modeling L Chen, K Lu, A Rajeswaran, K Lee, A Grover, M Laskin, P Abbeel, ... Advances in neural information processing systems, 2021 | 157 | 2021 |
| Decoupling representation learning from reinforcement learning A Stooke, K Lee, P Abbeel, M Laskin International Conference on Machine Learning, 9870-9879, 2021 | 117 | 2021 |
| Regularizing class-wise predictions via self-knowledge distillation S Yun, J Park, K Lee, J Shin Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 115 | 2020 |
| Overcoming catastrophic forgetting with unlabeled data in the wild K Lee, K Lee, J Shin, H Lee Proceedings of the IEEE/CVF International Conference on Computer Vision, 312-321, 2019 | 105 | 2019 |
| Network randomization: A simple technique for generalization in deep reinforcement learning K Lee, K Lee, J Shin, H Lee International Conference on Learning Representations, 2019 | 95 | 2019 |
| Sunrise: A simple unified framework for ensemble learning in deep reinforcement learning K Lee, M Laskin, A Srinivas, P Abbeel International Conference on Machine Learning, 6131-6141, 2021 | 74 | 2021 |
| Robust inference via generative classifiers for handling noisy labels K Lee, S Yun, K Lee, H Lee, B Li, J Shin International Conference on Machine Learning, 3763-3772, 2019 | 65 | 2019 |
| Hierarchical novelty detection for visual object recognition K Lee, K Lee, K Min, Y Zhang, J Shin, H Lee Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 56 | 2018 |
| Context-aware dynamics model for generalization in model-based reinforcement learning K Lee, Y Seo, S Lee, H Lee, J Shin International Conference on Machine Learning, 5757-5766, 2020 | 44 | 2020 |
| Confident Multiple Choice Learning K Lee, C Hwang, KS Park, J Shin Proceedings of the 34th International Conference on Machine Learning, 2017 | 44 | 2017 |
| State entropy maximization with random encoders for efficient exploration Y Seo, L Chen, J Shin, H Lee, P Abbeel, K Lee International Conference on Machine Learning, 2021 | 39 | 2021 |
| Learning to specialize with knowledge distillation for visual question answering J Mun, K Lee, J Shin, B Han Advances in neural information processing systems 31, 2018 | 31 | 2018 |
| PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training K Lee, L Smith, P Abbeel International Conference on Machine Learning, 2021 | 19 | 2021 |
| URLB: Unsupervised reinforcement learning benchmark M Laskin, D Yarats, H Liu, K Lee, A Zhan, K Lu, C Cang, L Pinto, P Abbeel Conference on Neural Information Processing Systems Datasets and Benchmarks …, 2021 | 16 | 2021 |
| Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble S Lee, Y Seo, K Lee, P Abbeel, J Shin Annual Conference on Robot Learning, 2021 | 13 | 2021 |
| Trajectory-wise multiple choice learning for dynamics generalization in reinforcement learning Y Seo, K Lee, I Clavera, T Kurutach, J Shin, P Abbeel Advances in neural information processing systems, 2020 | 10 | 2020 |
Jinwoo ShinAssociate Professor, KAIST AI & EEkaist.ac.kr의 이메일 확인됨
