Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients BK Petersen, M Landajuela, TN Mundhenk, CP Santiago, SK Kim, JT Kim arXiv preprint arXiv:1912.04871, 2019 | 297 | 2019 |
Reliable and explainable machine-learning methods for accelerated material discovery B Kailkhura, B Gallagher, S Kim, A Hiszpanski, TYJ Han npj Computational Materials 5 (1), 108, 2019 | 146 | 2019 |
Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences G Camps-Valls, D Tuia, XX Zhu, M Reichstein John Wiley & Sons, 2021 | 122 | 2021 |
Deep-Hurricane-Tracker: Tracking and Forecasting Extreme Climate Events S Kim, H Kim, J Lee, S Yoon, SE Kahou, K Kashinath, M Prabhat Winter conference of Applications of Computer Vision (WACV) 2019, 2019 | 105 | 2019 |
Discovering symbolic policies with deep reinforcement learning M Landajuela, BK Petersen, S Kim, CP Santiago, R Glatt, N Mundhenk, ... International Conference on Machine Learning, 5979-5989, 2021 | 100 | 2021 |
Personalized academic research paper recommendation system J Lee, K Lee, JG Kim arXiv preprint arXiv:1304.5457, 2013 | 79 | 2013 |
Multi-image super-resolution for remote sensing using deep recurrent networks MR Arefin, V Michalski, PL St-Charles, A Kalaitzis, S Kim, SE Kahou, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 67 | 2020 |
Vid-ode: Continuous-time video generation with neural ordinary differential equation S Park, K Kim, J Lee, J Choo, J Lee, S Kim, E Choi Proceedings of the AAAI Conference on Artificial Intelligence 35 (3), 2412-2422, 2021 | 53 | 2021 |
Deep-dust: Predicting concentrations of fine dust in Seoul using LSTM S Kim, J Lee,M, J Lee, J Seo Climate Informatics 2018, https://arxiv.org/abs/1901.10106, 2018 | 23 | 2018 |
Improving exploration in policy gradient search: Application to symbolic optimization M Landajuela, BK Petersen, SK Kim, CP Santiago, R Glatt, TN Mundhenk, ... arXiv preprint arXiv:2107.09158, 2021 | 20 | 2021 |
Potential role of urban forest in removing PM2. 5: A case study in Seoul by deep learning with satellite data A Lee, S Jeong, J Joo, CR Park, J Kim, S Kim Urban Climate 36, 100795, 2021 | 20 | 2021 |
Personalized academic paper recommendation system J Lee, K Lee, JG Kim, S Kim SRS’15, 2015 | 20 | 2015 |
Density functional theory calculations of magnetocrystalline anisotropy energies for (Fe1–xCox)2B LXB Markus Däne, Soo Kyung Kim, Michael P Surh, Daniel Åberg Journal of Physics: Condensed Matter 27 (26), 2015 | 19 | 2015 |
Massive scale deep learning for detecting extreme climate events SK Kim, S Ames, J Lee, C Zhang, AC Wilson, D Williams Climate Informatics 5, 2017 | 14 | 2017 |
An Interactive Visualization Platform for Deep Symbolic Regression JT Kim, S Kim, BK Petersen Proceedings of the International Joint Conferences on Artificial …, 2020 | 10 | 2020 |
Resolution reconstruction of climate data with pixel recursive model S Kim, S Ames, J Lee, C Zhang, AC Wilson, D Williams 2017 IEEE international conference on data mining workshops (ICDMW), 313-321, 2017 | 10 | 2017 |
Physics-guided Reinforcement Learning for 3D Molecular Structures Y Cho, S Kim, PP Li, MP Surh, TYJ Han, J Choo Workshop on Neurips 2019, Machine Learning and the Physical Sciences, https …, 2019 | 8 | 2019 |
Learning to Focus and Track Extreme Climate Events. S Kim, S Park, S Chung, J Lee, Y Lee, H Kim, M Prabhat, J Choo BMVC, 11, 2019 | 8 | 2019 |
A domain-independent agent architecture for adaptive operation in evolving open worlds S Mohan, W Piotrowski, R Stern, S Grover, S Kim, J Le, Y Sher, J de Kleer Artificial Intelligence, 104161, 2024 | 4 | 2024 |
ClimateNet: a machine learning dataset for climate science research M Prabhat, J Biard, S Ganguly, S Ames, K Kashinath, SK Kim, S Kahou, ... AGU fall meeting abstracts 2017, IN13E-01, 2017 | 4 | 2017 |