Sookyung Kim
Sookyung Kim
PARC (Palo Alto Research Center)
Vahvistettu sähköpostiosoite verkkotunnuksessa - Kotisivu
Personalized academic research paper recommendation system
J Lee, K Lee, JG Kim
arXiv preprint arXiv:1304.5457, 2013
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), 1-9, 2019
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
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
Personalized academic paper recommendation system
J Lee, K Lee, JG Kim, S Kim
SRS’15, 2015
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
BK Petersen, ML Larma, TN Mundhenk, CP Santiago, SK Kim, JT Kim
arXiv preprint arXiv:1912.04871, 2019
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
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
Massive scale deep learning for detecting extreme climate events
S Kim, S Ames, J Lee, C Zhang, AC Wilson, D Williams
Climate Informatics, 2017
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
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
Deep-dust: Predicting concentrations of fine dust in Seoul using LSTM
S Kim, J Lee,M, J Lee, J Seo
Climate Informatics 2018,, 2018
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
arXiv preprint arXiv:2010.08188, 2020
An Interactive Visualization Platform for Deep Symbolic Regression
JT Kim, S Kim, BK Petersen
Proceedings of the International Joint Conferences on Artificial …, 2020
ClimateNet: bringing the power of Deep Learning to the climate community via open datasets and architectures
M Prabhat, K Kashinath, T Kurth, M Mudigonda, A Mahesh, BA Toms, ...
AGU Fall Meeting Abstracts 2018, ED53E-0758, 2018
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
Assessing uncertainty in deep learning techniques that identify atmospheric rivers in climate simulations
A Mahesh, M Mudigonda, SK Kim, K Kashinath, S Kahou, V Michalski, ...
AGU Fall Meeting Abstracts 2017, IN11E-06, 2017
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
Optimizing 3D structure of H2O molecule using DDPG
SK Kim, P Li, JT Kim, P Karande, Y Han
Workshop on ICML 2019, 2019
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
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