| 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 | 1033 | 2018 |
| Training confidence-calibrated classifiers for detecting out-of-distribution samples K Lee, H Lee, K Lee, J Shin arXiv preprint arXiv:1711.09325, 2017 | 614 | 2017 |
| Network adiabatic theorem: an efficient randomized protocol for contention resolution S Rajagopalan, D Shah, J Shin ACM SIGMETRICS performance evaluation review 37 (1), 133-144, 2009 | 289 | 2009 |
| Csi: Novelty detection via contrastive learning on distributionally shifted instances J Tack, S Mo, J Jeong, J Shin Advances in neural information processing systems 33, 11839-11852, 2020 | 272 | 2020 |
| Instagan: Instance-aware image-to-image translation S Mo, M Cho, J Shin arXiv preprint arXiv:1812.10889, 2018 | 152 | 2018 |
| Distributed random access algorithm: scheduling and congestion control L Jiang, D Shah, J Shin, J Walrand IEEE Transactions on Information Theory 56 (12), 6182-6207, 2010 | 146 | 2010 |
| 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 | 145 | 2020 |
| Randomized scheduling algorithm for queueing networks D Shah, J Shin The Annals of Applied Probability 22 (1), 128-171, 2012 | 125 | 2012 |
| Randomized scheduling algorithm for queueing networks D Shah, J Shin The Annals of Applied Probability 22 (1), 128-171, 2012 | 125 | 2012 |
| Learning from failure: De-biasing classifier from biased classifier J Nam, H Cha, S Ahn, J Lee, J Shin Advances in Neural Information Processing Systems 33, 20673-20684, 2020 | 123 | 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 | 118 | 2019 |
| Self-supervised label augmentation via input transformations H Lee, SJ Hwang, J Shin International Conference on Machine Learning, 5714-5724, 2020 | 117* | 2020 |
| Neural adaptive content-aware internet video delivery H Yeo, Y Jung, J Kim, J Shin, D Han 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2018 | 116 | 2018 |
| Network randomization: A simple technique for generalization in deep reinforcement learning K Lee, K Lee, J Shin, H Lee arXiv preprint arXiv:1910.05396, 2019 | 109 | 2019 |
| Freeze the discriminator: a simple baseline for fine-tuning gans S Mo, M Cho, J Shin arXiv preprint arXiv:2002.10964, 2020 | 106 | 2020 |
| Learning what and where to transfer Y Jang, H Lee, SJ Hwang, J Shin International Conference on Machine Learning, 3030-3039, 2019 | 98 | 2019 |
| M2m: Imbalanced classification via major-to-minor translation J Kim, J Jeong, J Shin Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 96 | 2020 |
| Large-scale log-determinant computation through stochastic Chebyshev expansions I Han, D Malioutov, J Shin International Conference on Machine Learning, 908-917, 2015 | 89 | 2015 |
| 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 | 75 | 2019 |
| Approximating spectral sums of large-scale matrices using stochastic Chebyshev approximations I Han, D Malioutov, H Avron, J Shin SIAM Journal on Scientific Computing 39 (4), A1558-A1585, 2017 | 74 | 2017 |
