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Sungsoo Ahn
Sungsoo Ahn
Verified email at postech.ac.kr - Homepage
Title
Cited by
Cited by
Year
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
5522019
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
2662020
Layer-adaptive sparsity for the magnitude-based pruning
J Lee, S Park, S Mo, S Ahn, J Shin
arXiv preprint arXiv:2010.07611, 2020
842020
Guiding Deep Molecular Optimization with Genetic Exploration
S Ahn, J Kim, H Lee, J Shin
Advances in Neural Information Processing Systems 33, 2020
552020
Learning what to defer for maximum independent sets
S Ahn, Y Seo, J Shin
International Conference on Machine Learning, 134-144, 2020
442020
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
202021
Self-improved retrosynthetic planning
J Kim, S Ahn, H Lee, J Shin
International Conference on Machine Learning, 5486-5495, 2021
162021
Learning debiased classifier with biased committee
N Kim, S Hwang, S Ahn, J Park, S Kwak
Advances in Neural Information Processing Systems 35, 18403-18415, 2022
152022
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
152020
Sythesis of MCMC and Belief Propagation
S Ahn, M Chertkov, J Shin
Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2016
132016
A closer look at the intervention procedure of concept bottleneck models
S Shin, Y Jo, S Ahn, N Lee
arXiv preprint arXiv:2302.14260, 2023
122023
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
122021
Spanning tree-based graph generation for molecules
S Ahn, B Chen, T Wang, L Song
International Conference on Learning Representations, 2021
112021
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
82015
Abstract reasoning via logic-guided generation
S Yu, S Mo, S Ahn, J Shin
arXiv preprint arXiv:2107.10493, 2021
72021
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
62020
Gauging variational inference
S Ahn, M Chertkov, J Shin
Advances in Neural Information Processing Systems, 2881-2890, 2017
62017
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
52018
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
52013
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
42021
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