| Fast AutoAugment S Lim, I Kim, T Kim, C Kim, S Kim Advances in Neural Information Processing Systems 32, 6662-6672, 2019 | 461 | 2019 |
| Uncertainty-aware learning from demonstration using mixture density networks with sampling-free variance modeling S Choi, K Lee, S Lim, S Oh 2018 IEEE International Conference on Robotics and Automation (ICRA), 6915-6922, 2018 | 85 | 2018 |
| Neural stain-style transfer learning using gan for histopathological images H Cho, S Lim, G Choi, H Min ACML Workshop on MLAIP, 2017 | 77 | 2017 |
| Scalable Neural Architecture Search for 3D Medical Image Segmentation S Kim, I Kim, S Lim, W Baek, C Kim, H Cho, B Yoon, T Kim Medical Image Computing and Computer Assisted Intervention 22, 220-228, 2019 | 61 | 2019 |
| An Lq (Lp)-theory for the time fractional evolution equations with variable coefficients I Kim, KH Kim, S Lim Advances in Mathematics 306, 123-176, 2017 | 60 | 2017 |
| Asymptotic behaviors of fundamental solution and its derivatives related to space-time fractional differential equations KH Kim, S Lim Journal of the Korean Mathematical Society 53 (4), 929-967, 2016 | 35* | 2016 |
| Monte Carlo Tree Search in Continuous Spaces Using Voronoi Optimistic Optimization with Regret Bounds B Kim, K Lee, S Lim, LP Kaelbling, T Lozano-Pérez Proceedings of the AAAI Conference on Artificial Intelligence 34, 2020 | 29 | 2020 |
| torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models C Kim, H Lee, M Jeong, W Baek, B Yoon, I Kim, S Lim, S Kim arXiv preprint arXiv:2004.09910, 2020 | 24 | 2020 |
| Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Networks S Choi, S Hong, K Lee, S Lim Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 16* | 2020 |
| Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning K Lee, S Kim, S Lim, S Choi, S Oh arXiv preprint arXiv:1902.00137, 2019 | 15 | 2019 |
| A Sobolev space theory for stochastic partial differential equations with time-fractional derivatives I Kim, K Kim, S Lim The Annals of Probability 47 (4), 2087-2139, 2019 | 14 | 2019 |
| An Lq(Lp)-Theory for Parabolic Pseudo-Differential Equations: Calderón-Zygmund Approach I Kim, S Lim, KH Kim Potential Analysis 45 (3), 463-483, 2016 | 13 | 2016 |
| Generalized Tsallis Entropy Reinforcement Learning and Its Application to Soft Mobile Robots K Lee, S Kim, S Lim, S Choi, M Hong, J Kim, YL Park, S Oh Robotics: Science and Systems Foundation, 2020 | 12 | 2020 |
| Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards K Lee, H Yang, S Lim, S Oh Advances in Neural Information Processing Systems 33, 2020 | 11 | 2020 |
| Parabolic Littlewood–Paley inequality for a class of time-dependent pseudo-differential operators of arbitrary order, and applications to high-order stochastic PDE I Kim, KH Kim, S Lim Journal of Mathematical Analysis and Applications 436 (2), 1023-1047, 2016 | 11 | 2016 |
| Parabolic BMO estimates for pseudo-differential operators of arbitrary order I Kim, KH Kim, S Lim Journal of Mathematical Analysis and Applications 427 (2), 557-580, 2015 | 11 | 2015 |
| Evaluation of conditional treatment effects of adjuvant treatments on patients with synovial sarcoma using Bayesian subgroup analysis SW Seo, J Kim, J Son, S Lim BMC Medical Informatics and Decision Making 20 (1), 1-12, 2020 | 6 | 2020 |
| A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone A Bogyrbayeva, T Yoon, H Ko, S Lim, H Yun, C Kwon arXiv preprint arXiv:2112.12545, 2021 | 4* | 2021 |
| Neural Bootstrapper M Shin, H Cho, H Min, S Lim Thirty-Fifth Conference on Neural Information Processing Systems, 2021 | 1 | 2021 |
| Supplementary Material for Generalized Tsallis Entropy Reinforcement Learning and Its Application to Soft Mobile Robots K Lee, S Kim, S Lim, S Choi, M Hong, J Kim, YL Park, S Oh Technical report, Department of Electrical and Computer Engineering, Seoul …, 2020 | 1 | 2020 |
Kyungjae LeeChung-Ang Universitycau.ac.kr의 이메일 확인됨
