| Variational information distillation for knowledge transfer S Ahn, SX Hu, A Damianou, ND Lawrence, Z Dai Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 444 | 2019 |
| 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, 2020 | 187 | 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 | 55 | 2020 |
| Guiding Deep Molecular Optimization with Genetic Exploration S Ahn, J Kim, H Lee, J Shin Advances in Neural Information Processing Systems 33, 2020 | 44 | 2020 |
| Learning what to defer for maximum independent sets S Ahn, Y Seo, J Shin International Conference on Machine Learning, 134-144, 2020 | 25 | 2020 |
| Self-improved retrosynthetic planning J Kim, S Ahn, H Lee, J Shin International Conference on Machine Learning, 5486-5495, 2021 | 13 | 2021 |
| Sythesis of MCMC and Belief Propagation S Ahn, M Chertkov, J Shin Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2016 | 13 | 2016 |
| Roma: Robust model adaptation for offline model-based optimization S Yu, S Ahn, L Song, J Shin Advances in Neural Information Processing Systems 34, 4619-4631, 2021 | 11 | 2021 |
| QTRAN++: improved value transformation for cooperative multi-agent reinforcement learning K Son, S Ahn, RD Reyes, J Shin, Y Yi arXiv preprint arXiv:2006.12010, 2020 | 10 | 2020 |
| Learning debiased classifier with biased committee N Kim, S Hwang, S Ahn, J Park, S Kwak arXiv preprint arXiv:2206.10843, 2022 | 8 | 2022 |
| 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 | 8 | 2021 |
| Spanning tree-based graph generation for molecules S Ahn, B Chen, T Wang, L Song International Conference on Learning Representations, 2021 | 8 | 2021 |
| Minimum weight perfect matching via blossom belief propagation S Ahn, S Park, M Chertkov, J Shin Advances in Neural Information Processing Systems, 1288-1296, 2015 | 7 | 2015 |
| Gauging variational inference S Ahn, M Chertkov, J Shin Advances in Neural Information Processing Systems, 2881-2890, 2017 | 6 | 2017 |
| Abstract reasoning via logic-guided generation S Yu, S Mo, S Ahn, J Shin arXiv preprint arXiv:2107.10493, 2021 | 5 | 2021 |
| 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 | 5 | 2020 |
| Gauged mini-bucket elimination for approximate inference S Ahn, M Chertkov, J Shin, A Weller International Conference on Artificial Intelligence and Statistics, 10-19, 2018 | 5 | 2018 |
| A graphical transformation for belief propagation: Maximum weight matchings and odd-sized cycles J Shin, AE Gelfand, M Chertkov Advances in Neural Information Processing Systems 26, 2013 | 5 | 2013 |
| Bucket renormalization for approximate inference S Ahn, M Chertkov, A Weller, J Shin International Conference on Machine Learning, 109-118, 2018 | 4 | 2018 |
| What Makes Better Augmentation Strategies? Augment Difficult but Not too Different J Kim, D Kang, S Ahn, J Shin International Conference on Learning Representations, 2021 | 2 | 2021 |
Jinwoo ShinKAIST Endowed Chair ProfessorVerified email at kaist.ac.kr
