| Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks J Lee, Y Lee, J Kim, A Kosiorek, S Choi, YW Teh International Conference on Machine Learning, 3744-3753, 2019 | 1483 | 2019 |
| Learning to propagate labels: Transductive propagation network for few-shot learning Y Liu, J Lee, M Park, S Kim, E Yang, SJ Hwang, Y Yang arXiv preprint arXiv:1805.10002, 2018 | 961 | 2018 |
| Adversarial purification with score-based generative models J Yoon, SJ Hwang, J Lee International Conference on Machine Learning, 12062-12072, 2021 | 181 | 2021 |
| Uncertainty-aware attention for reliable interpretation and prediction J Heo, HB Lee, S Kim, J Lee, KJ Kim, E Yang, SJ Hwang Advances in neural information processing systems 31, 2018 | 110 | 2018 |
| Setvae: Learning hierarchical composition for generative modeling of set-structured data J Kim, J Yoo, J Lee, S Hong Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 89 | 2021 |
| Bootstrapping neural processes J Lee, Y Lee, J Kim, E Yang, SJ Hwang, YW Teh Advances in neural information processing systems 33, 6606-6615, 2020 | 55 | 2020 |
| Diversity matters when learning from ensembles G Nam, J Yoon, Y Lee, J Lee Advances in neural information processing systems 34, 8367-8377, 2021 | 47 | 2021 |
| A multi-mode modulator for multi-domain few-shot classification Y Liu, J Lee, L Zhu, L Chen, H Shi, Y Yang Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 44 | 2021 |
| Towards safe self-distillation of internet-scale text-to-image diffusion models S Kim, S Jung, B Kim, M Choi, J Shin, J Lee arXiv preprint arXiv:2307.05977, 2023 | 32 | 2023 |
| Learning to perturb word embeddings for out-of-distribution QA S Lee, M Kang, J Lee, SJ Hwang arXiv preprint arXiv:2105.02692, 2021 | 21 | 2021 |
| Probabilistic imputation for time-series classification with missing data SH Kim, H Kim, E Yun, H Lee, J Lee, J Lee International Conference on Machine Learning, 16654-16667, 2023 | 20 | 2023 |
| Deep amortized clustering J Lee, Y Lee, YW Teh arXiv preprint arXiv:1909.13433, 2019 | 20 | 2019 |
| DropMax: Adaptive variational softmax HB Lee, J Lee, S Kim, E Yang, SJ Hwang Advances in Neural Information Processing Systems 31, 2018 | 20 | 2018 |
| Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models J Lee, LF James, S Choi Advances in Neural Information Processing Systems, 2016 | 19 | 2016 |
| Online video segmentation by bayesian split-merge clustering J Lee, S Kwak, B Han, S Choi Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012 | 19 | 2012 |
| Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with Double Power-law Behavior F Ayed, J Lee, F Caron International Conference on Machine Learning, 2019 | 17 | 2019 |
| Decoupled training for long-tailed classification with stochastic representations G Nam, S Jang, J Lee arXiv preprint arXiv:2304.09426, 2023 | 16 | 2023 |
| Exploring the role of mean teachers in self-supervised masked auto-encoders Y Lee, J Willette, J Kim, J Lee, SJ Hwang arXiv preprint arXiv:2210.02077, 2022 | 15 | 2022 |
| Deep mixed effect model using Gaussian processes: a personalized and reliable prediction for healthcare I Chung, S Kim, J Lee, KJ Kim, SJ Hwang, E Yang Proceedings of the AAAI conference on artificial intelligence 34 (04), 3649-3657, 2020 | 14 | 2020 |
| On divergence measures for bayesian pseudocoresets B Kim, J Choi, S Lee, Y Lee, JW Ha, J Lee Advances in Neural Information Processing Systems 35, 757-767, 2022 | 12 | 2022 |
Sung Ju HwangKAIST, DeepAutoE-mail megerősítve itt: kaist.ac.kr