Sungsoo Ahn
Sungsoo Ahn
Verified email at kaist.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
1382019
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
17*2020
Sythesis of MCMC and Belief Propagation
S Ahn, M Chertkov, J Shin
Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2016
122016
Gauging variational inference
S Ahn, M Chertkov, J Shin
Advances in Neural Information Processing Systems, 2881-2890, 2017
62017
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
62015
Guiding Deep Molecular Optimization with Genetic Exploration
S Ahn, J Kim, H Lee, J Shin
Advances in Neural Information Processing Systems 33, 2020
52020
Variational information distillation for knowledge transfer. 2019 IEEE
S Ahn, SX Hu, AC Damianou, ND Lawrence, Z Dai
CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9155-9163, 2019
52019
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
S Ahn, M Chertkov, AE Gelfand, S Park, J Shin
arXiv preprint arXiv:1306.1167, 2013
52013
Learning What to Defer for Maximum Independent Sets
S Ahn, Y Seo, J Shin
International Conference on Machine Learning, 134-144, 2020
32020
Bucket renormalization for approximate inference
S Ahn, M Chertkov, A Weller, J Shin
International Conference on Machine Learning, 109-118, 2018
32018
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
22017
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, 2020
12020
Layer-adaptive Sparsity for the Magnitude-based Pruning
J Lee, S Park, S Mo, S Ahn, J Shin
International Conference on Learning Representations, 2020
12020
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
12020
QOPT: Optimistic Value Function Decentralization for Cooperative Multi-Agent Reinforcement Learning
K Son, S Ahn, R Delos Reyes, J Shin, Y Yi
arXiv e-prints, arXiv: 2006.12010, 2020
12020
Abstract Reasoning via Logic-guided Generation
S Yu, S Mo, S Ahn, J Shin
arXiv preprint arXiv:2107.10493, 2021
2021
Self-Improved Retrosynthetic Planning
J Kim, S Ahn, H Lee, J Shin
arXiv preprint arXiv:2106.04880, 2021
2021
RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning
H Lee, S Ahn, SW Seo, YY Song, SJ Hwang, E Yang, J Shin
arXiv preprint arXiv:2105.00795, 2021
2021
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Articles 1–19