no code implementations • 22 Dec 2023 • Siqi Chen, Bin Shan, Ye Li
Physics-informed neural networks (PINNs) have shown promising potential for solving partial differential equations (PDEs) using deep learning.
1 code implementation • 29 Sep 2023 • Siqi Chen, Pierre-Philippe Dechant, Yang-Hui He, Elli Heyes, Edward Hirst, Dmitrii Riabchenko
This provides the perfect setup for machine learning, and indeed we see that the datasets can be machine learned to very high accuracy.
1 code implementation • 13 May 2023 • Yu Zhang, Siqi Chen, Mingdao Wang, Xianlin Zhang, Chuang Zhu, Yue Zhang, Xueming Li
Extensive experiments demonstrate that our method outperforms other methods in maintaining temporal consistency both qualitatively and quantitatively.
1 code implementation • 13 Apr 2023 • Siqi Chen, Xueming Li, Xianlin Zhang, Mingdao Wang, Yu Zhang, Yue Zhang
Previous methods search for correspondence across the entire reference image, and this type of global matching is easy to get mismatch.
no code implementations • 27 Mar 2023 • Siqi Chen, Xueming Li, Xianlin Zhang, Mingdao Wang, Yu Zhang, Jiatong Han, Yue Zhang
Exemplar-based video colorization is an essential technique for applications like old movie restoration.
no code implementations • 23 Apr 2022 • Jiahao Ma, Guotong Xu, Le Ao, Siqi Chen, Jingze Liu
The structure domain was analyzed by Blast.
no code implementations • 18 Sep 2018 • Chengwei Zhang, Xiaohong Li, Jianye Hao, Siqi Chen, Karl Tuyls, Zhiyong Feng, Wanli Xue, Rong Chen
Although many reinforcement learning methods have been proposed for learning the optimal solutions in single-agent continuous-action domains, multiagent coordination domains with continuous actions have received relatively few investigations.
no code implementations • 8 Mar 2018 • Chengwei Zhang, Xiaohong Li, Jianye Hao, Siqi Chen, Karl Tuyls, Wanli Xue
In multiagent environments, the capability of learning is important for an agent to behave appropriately in face of unknown opponents and dynamic environment.