| 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 | 992 | 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 | 769 | 2018 |
| 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 | 92 | 2018 |
| Adversarial purification with score-based generative models J Yoon, SJ Hwang, J Lee International Conference on Machine Learning, 12062-12072, 2021 | 73 | 2021 |
| 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 | 39 | 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 | 31 | 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 | 25 | 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 | 23 | 2021 |
| Deep amortized clustering J Lee, Y Lee, YW Teh arXiv preprint arXiv:1909.13433, 2019 | 17 | 2019 |
| 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 | 17 | 2012 |
| DropMax: Adaptive variational softmax HB Lee, J Lee, S Kim, E Yang, SJ Hwang Advances in Neural Information Processing Systems 31, 2018 | 16 | 2018 |
| Learning to perturb word embeddings for out-of-distribution QA S Lee, M Kang, J Lee, SJ Hwang arXiv preprint arXiv:2105.02692, 2021 | 15 | 2021 |
| 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 | 14 | 2016 |
| 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 | 12 | 2019 |
| 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 | 10 | 2020 |
| Cost-effective interactive attention learning with neural attention processes J Heo, J Park, H Jeong, KJ Kim, J Lee, E Yang, SJ Hwang International Conference on Machine Learning, 4228-4238, 2020 | 9 | 2020 |
| Adaptive network sparsification with dependent variational beta-bernoulli dropout J Lee, S Kim, J Yoon, HB Lee, E Yang, SJ Hwang arXiv preprint arXiv:1805.10896, 2018 | 9 | 2018 |
| Bayesian inference on random simple graphs with power law degree distributions J Lee, C Heaukulani, Z Ghahramani, LF James, S Choi International Conference on Machine Learning, 2004-2013, 2017 | 8 | 2017 |
| Bayesian hierarchical clustering with exponential family: small-variance asymptotics and reducibility J Lee, S Choi Artificial Intelligence and Statistics, 581-589, 2015 | 8 | 2015 |
| Benefits of stochastic weight averaging in developing neural network radiation scheme for numerical weather prediction HJ Song, S Roh, J Lee, G Nam, E Yun, J Yoon, PS Kim Journal of Advances in Modeling Earth Systems 14 (10), e2021MS002921, 2022 | 7 | 2022 |
Sung Ju HwangKAISTAdresse e-mail validée de kaist.ac.kr
