| Self-supervised Label Augmentation via Input Transformations H Lee, SJ Hwang, J Shin International Conference on Machine Learning, 2020 | 281* | 2020 |
| Learning What and Where to Transfer Y Jang*, H Lee*, SJ Hwang, J Shin International Conference on Machine Learning, 2019 | 178 | 2019 |
| Guiding deep molecular optimization with genetic exploration S Ahn, J Kim, H Lee, J Shin Advances in neural information processing systems 33, 12008-12021, 2020 | 88 | 2020 |
| GTA: Graph Truncated Attention for Retrosynthesis SW Seo, YY Song, JY Yang, S Bae, H Lee, J Shin, SJ Hwang, E Yang Proceedings of the AAAI Conference on Artificial Intelligence 35 (1), 531-539, 2021 | 63 | 2021 |
| Patch-Level Representation Learning for Self-Supervised Vision Transformers S Yun, H Lee, J Kim, J Shin Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 61 | 2022 |
| Improving transferability of representations via augmentation-aware self-supervision H Lee, K Lee, K Lee, H Lee, J Shin Advances in Neural Information Processing Systems 34, 17710-17722, 2021 | 55 | 2021 |
| STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables J Nam, J Tack, K Lee, H Lee, J Shin arXiv preprint arXiv:2303.00918, 2023 | 27 | 2023 |
| Self-improved retrosynthetic planning J Kim, S Ahn, H Lee, J Shin International Conference on Machine Learning, 5486-5495, 2021 | 26 | 2021 |
| Anytime Neural Prediction via Slicing Networks Vertically H Lee, J Shin arXiv preprint arXiv:1807.02609, 2018 | 23 | 2018 |
| Unsupervised Meta-learning via Few-shot Pseudo-supervised Contrastive Learning H Jang, H Lee, J Shin arXiv preprint arXiv:2303.00996, 2023 | 20 | 2023 |
| RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning H Lee, S Ahn, SW Seo, YY Song, E Yang, SJ Hwang, J Shin arXiv preprint arXiv:2105.00795, 2021 | 19 | 2021 |
| Guiding Energy-based Models via Contrastive Latent Variables H Lee, J Jeong, S Park, J Shin arXiv preprint arXiv:2303.03023, 2023 | 16 | 2023 |
| Meta-Learning with Self-Improving Momentum Target J Tack, J Park, H Lee, J Lee, J Shin Advances in Neural Information Processing Systems 35, 6318-6332, 2022 | 9 | 2022 |
| Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models S Choi, H Lee, H Lee, M Lee Advances in Neural Information Processing Systems 36, 2024 | 6 | 2024 |
| Few-shot Anomaly Detection via Personalization S Kwak, J Jeong, H Lee, W Kim, D Seo, W Yun, W Lee, J Shin IEEE Access, 2024 | 2 | 2024 |
| Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning S Cho, S Cho, S Park, H Lee, H Lee, M Lee arXiv preprint arXiv:2309.04082, 2023 | 1 | 2023 |
| Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck J Jeong, S Yu, H Lee, J Shin Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 1 | 2023 |
| Diffusion-based Semantic-Discrepant Outlier Generation for Out-of-Distribution Detection S Yoon, S Yoon, H Lee, S Han, YS Sim, K Lee, H Cho, W Lim NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI, 2023 | | 2023 |
