no code implementations • 30 Nov 2023 • Chengwei Zhang, Yushuang Zhai, Ziyang Gong, Hongliang Duan, Yuan-Bin She, Yun-Fang Yang, An Su
This study showcases the potential of utilizing databases of drug-like small molecules and chemical reactions to pretrain the BERT model, enhancing its performance in the virtual screening of organic materials.
1 code implementation • 22 Apr 2021 • Chengwei Zhang, Shan Jin, Wanli Xue, Xiaofei Xie, ShengYong Chen, Rong Chen
To this, we model the traffic control problem as a partially observable weak cooperative traffic model (PO-WCTM) to optimize the overall traffic situation of a group of intersections.
no code implementations • 30 Jan 2021 • Chengwei Zhang, Yangzhou Jiang, Wei zhang, Chengyu Gu
The proposed model is capable to capture the dynamic changes in users knowledge states at different temporal-ranges, and provides an efficient and powerful way to combine local and global features to make predictions.
3 code implementations • 27 May 2020 • Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng, ShiLiang Pu, Yi Niu, Fei Wu, Futai Zou
Arbitrary text appearance poses a great challenge in scene text recognition tasks.
no code implementations • 7 Aug 2019 • Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng, Yi Niu, ShiLiang Pu, Fei Wu, Futai Zou
The second module is a specific classifier for mining trivial or incomplete action regions, which is trained on the shared features after erasing the seeded regions activated by SSG.
Action Detection Weakly-supervised Temporal Action Localization +1
no code implementations • 19 Nov 2018 • Yunlu Xu, Chengwei Zhang, Zhanzhan Cheng, Jianwen Xie, Yi Niu, ShiLiang Pu, Fei Wu
Finally, we transform the output of recurrent neural network into the corresponding action distribution.
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.