| Conditional molecular design with deep generative models S Kang, K Cho Journal of Chemical Information and Modeling 59 (1), 43-52, 2019 | 196 | 2019 |
| Molecular geometry prediction using a deep generative graph neural network E Mansimov, O Mahmood, S Kang, K Cho Scientific Reports 9, 20381, 2019 | 172 | 2019 |
| Deep-learning-based inverse design model for intelligent discovery of organic molecules K Kim, S Kang, J Yoo, Y Kwon, Y Nam, D Lee, I Kim, YS Choi, Y Jung, ... npj Computational Materials 4, 67, 2018 | 96 | 2018 |
| An efficient and effective ensemble of support vector machines for anti-diabetic drug failure prediction S Kang, P Kang, T Ko, S Cho, S Rhee, KS Yu Expert Systems with Applications 42 (9), 4265-4273, 2015 | 76 | 2015 |
| Constructing a multi-class classifier using one-against-one approach with different binary classifiers S Kang, S Cho, P Kang Neurocomputing 149, 677-682, 2015 | 75 | 2015 |
| Approximating support vector machine with artificial neural network for fast prediction S Kang, S Cho Expert Systems with Applications 41 (10), 4989-4995, 2014 | 71 | 2014 |
| Multi-class classification via heterogeneous ensemble of one-class classifiers S Kang, S Cho, P Kang Engineering Applications of Artificial Intelligence 43, 35-43, 2015 | 68 | 2015 |
| Active learning of convolutional neural network for cost-effective wafer map pattern classification J Shim, S Kang, S Cho IEEE Transactions on Semiconductor Manufacturing 33 (2), 258-266, 2020 | 61 | 2020 |
| An intelligent virtual metrology system with adaptive update for semiconductor manufacturing S Kang, P Kang Journal of Process Control 52, 66-74, 2017 | 53 | 2017 |
| k-nearest neighbor learning with graph neural networks S Kang Mathematics 9 (8), 830, 2021 | 50 | 2021 |
| Using wafer map features to better predict die-level failures in final test S Kang, S Cho, D An, J Rim IEEE Transactions on Semiconductor Manufacturing 28 (3), 431-437, 2015 | 50 | 2015 |
| Neural message passing for NMR chemical shift prediction Y Kwon, D Lee, YS Choi, M Kang, S Kang Journal of Chemical Information and Modeling 60 (4), 2024-2030, 2020 | 44 | 2020 |
| Mining the relationship between production and customer service data for failure analysis of industrial products S Kang, E Kim, J Shim, S Cho, W Chang, J Kim Computers & Industrial Engineering 106, 137-146, 2017 | 44 | 2017 |
| On effectiveness of transfer learning approach for neural network-based virtual metrology modeling S Kang IEEE Transactions on Semiconductor Manufacturing 31 (1), 149-155, 2018 | 38 | 2018 |
| Efficient feature selection based on random forward search for virtual metrology modeling S Kang, D Kim, S Cho IEEE Transactions on Semiconductor Manufacturing 29 (4), 391-398, 2016 | 38 | 2016 |
| Evolutionary design of molecules based on deep learning and a genetic algorithm Y Kwon, S Kang, YS Choi, I Kim Scientific Reports 11, 17304, 2021 | 35 | 2021 |
| Joint modeling of classification and regression for improving faulty wafer detection in semiconductor manufacturing S Kang Journal of Intelligent Manufacturing 31 (2), 319-326, 2020 | 35 | 2020 |
| Uncertainty-aware prediction of chemical reaction yields with graph neural networks Y Kwon, D Lee, YS Choi, S Kang Journal of Cheminformatics 14, 2, 2022 | 34 | 2022 |
| Rotation-invariant wafer map pattern classification with convolutional neural networks S Kang IEEE Access 8, 170650-170658, 2020 | 34 | 2020 |
| Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks S Kang Artificial Intelligence in Medicine 85, 1-6, 2018 | 34 | 2018 |
sungzoon choSeoul National UniversityVerified email at snu.ac.kr
