no code implementations • 28 Dec 2023 • Bangyi Zhao, Weixia Xu, Jihong Guan, Shuigeng Zhou
Following that, we conduct graph structure learning on the MSG (i. e., molecule-level graph structure learning) to get the final molecular embeddings, which are the results of fusing both GNN encoded molecular representations and the relationships among molecules, i. e., combining both intra-molecule and inter-molecule information.
no code implementations • 2 Jun 2020 • Shasha Guo, Lei Wang, Xiaofan Chen, Limeng Zhang, Ziyang Kang, Weixia Xu
We not only give the visual denoising effect of the filter but also use two metrics for quantitatively analyzing the filter's performance.
1 code implementation • 14 May 2020 • Yemao Xu, Dezun Dong, Weixia Xu, Xiangke Liao
To scale out to achieve faster training speed, two update algorithms are mainly applied in the distributed training process, i. e. the Synchronous SGD algorithm (SSGD) and Asynchronous SGD algorithm (ASGD).
no code implementations • 8 Apr 2020 • Shasha Guo, Ziyang Kang, Lei Wang, Limeng Zhang, Xiaofan Chen, Shiming Li, Weixia Xu
Neuromorphic event-based dynamic vision sensors (DVS) have much faster sampling rates and a higher dynamic range than frame-based imagers.
Emerging Technologies Signal Processing
no code implementations • 7 Apr 2020 • Shuo Tian, Lianhua Qu, Kai Hu, Nan Li, Lei Wang, Weixia Xu
By exploring the design space in network architectures and parameters, recent works have demonstrated great potential for improving the accuracy of LSM model with low complexity.
no code implementations • 6 Apr 2020 • Shasha Guo, Lianhua Qu, Lei Wang, Shuo Tian, Shiming Li, Weixia Xu
To mitigate the difficulty in effectively dealing with huge input spaces of LSM, and to find that whether input reduction can enhance LSM performance, we explore several input patterns, namely fullscale, scanline, chessboard, and patch.
no code implementations • 1 Aug 2019 • Zhaosong Huang, Ye Zhao, Wei Chen, Shengjie Gao, Kejie Yu, Weixia Xu, Mingjie Tang, Minfeng Zhu, Mingliang Xu
Visual querying is essential for interactively exploring massive trajectory data.