| Distribution aligning refinery of pseudo-label for imbalanced semi-supervised learning J Kim, Y Hur, S Park, E Yang, SJ Hwang, J Shin Advances in neural information processing systems 33, 14567-14579, 2020 | 190 | 2020 |
| Layer-adaptive sparsity for the magnitude-based pruning J Lee, S Park, S Mo, S Ahn, J Shin arXiv preprint arXiv:2010.07611, 2020 | 166 | 2020 |
| Minimum width for universal approximation S Park, C Yun, J Lee, J Shin arXiv preprint arXiv:2006.08859, 2020 | 148 | 2020 |
| Lookahead: A far-sighted alternative of magnitude-based pruning S Park, J Lee, S Mo, J Shin arXiv preprint arXiv:2002.04809, 2020 | 107 | 2020 |
| Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability S Park, D Deka, M Chertkov Power Systems Computation Conference (PSCC), 2018, 2018 | 90 | 2018 |
| Smoothmix: Training confidence-calibrated smoothed classifiers for certified robustness J Jeong, S Park, M Kim, HC Lee, DG Kim, J Shin Advances in Neural Information Processing Systems 34, 30153-30168, 2021 | 52 | 2021 |
| Neural networks efficiently learn low-dimensional representations with sgd A Mousavi-Hosseini, S Park, M Girotti, I Mitliagkas, MA Erdogdu arXiv preprint arXiv:2209.14863, 2022 | 50 | 2022 |
| Learning bounds for risk-sensitive learning J Lee, S Park, J Shin Advances in Neural Information Processing Systems 33, 13867-13879, 2020 | 50 | 2020 |
| Learning with end-users in distribution grids: Topology and parameter estimation S Park, D Deka, S Backhaus, M Chertkov IEEE Transactions on Control of Network Systems 7 (3), 1428-1440, 2020 | 40 | 2020 |
| Provable memorization via deep neural networks using sub-linear parameters S Park, J Lee, C Yun, J Shin Conference on Learning Theory, 3627-3661, 2021 | 32 | 2021 |
| Max-product belief propagation for linear programming: applications to combinatorial optimization S Park, J Shin arXiv preprint arXiv:1412.4972, 2014 | 13 | 2014 |
| Guiding Energy-based Models via Contrastive Latent Variables H Lee, J Jeong, S Park, J Shin arXiv preprint arXiv:2303.03023, 2023 | 12 | 2023 |
| Generalization Bounds for Stochastic Gradient Descent via Localized -Covers S Park, U Simsekli, MA Erdogdu Advances in Neural Information Processing Systems 35, 2790-2802, 2022 | 10 | 2022 |
| A deeper look at the layerwise sparsity of magnitude-based pruning J Lee, S Park, S Mo, S Ahn, J Shin arXiv preprint arXiv:2010.07611 2 (3), 2020 | 9 | 2020 |
| Rapid mixing Swendsen-Wang sampler for stochastic partitioned attractive models S Park, Y Jang, A Galanis, J Shin, D Stefankovic, E Vigoda Artificial Intelligence and Statistics, 440-449, 2017 | 9 | 2017 |
| Minimum weight perfect matching via blossom belief propagation SS Ahn, S Park, M Chertkov, J Shin Advances in neural information processing systems 28, 2015 | 8 | 2015 |
| Convergence and correctness of max-product belief propagation for linear programming S Park, J Shin SIAM Journal on Discrete Mathematics 31 (3), 2228-2246, 2017 | 7 | 2017 |
| Spectral approximate inference S Park, E Yang, SY Yun, J Shin International Conference on Machine Learning, 5052-5061, 2019 | 5 | 2019 |
| Maximum weight matching using odd-sized cycles: Max-product belief propagation and half-integrality S Ahn, M Chertkov, AE Gelfand, S Park, J Shin IEEE Transactions on Information Theory 64 (3), 1471-1480, 2017 | 3 | 2017 |
| Practical message-passing framework for large-scale combinatorial optimization I Cho, S Park, S Park, D Han, J Shin 2015 IEEE international conference on big data (Big Data), 24-31, 2015 | 3 | 2015 |
