1 code implementation • 14 May 2024 • Zhimin Li, Jianwei Zhang, Qin Lin, Jiangfeng Xiong, Yanxin Long, Xinchi Deng, Yingfang Zhang, Xingchao Liu, Minbin Huang, Zedong Xiao, Dayou Chen, Jiajun He, Jiahao Li, Wenyue Li, Chen Zhang, Rongwei Quan, Jianxiang Lu, Jiabin Huang, Xiaoyan Yuan, Xiaoxiao Zheng, Yixuan Li, Jihong Zhang, Chao Zhang, Meng Chen, Jie Liu, Zheng Fang, Weiyan Wang, Jinbao Xue, Yangyu Tao, Jianchen Zhu, Kai Liu, Sihuan Lin, Yifu Sun, Yun Li, Dongdong Wang, Mingtao Chen, Zhichao Hu, Xiao Xiao, Yan Chen, Yuhong Liu, Wei Liu, Di Wang, Yong Yang, Jie Jiang, Qinglin Lu
For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images.
no code implementations • 18 Mar 2024 • Hao Wu, Fan Xu, Yifan Duan, Ziwei Niu, Weiyan Wang, Gaofeng Lu, Kun Wang, Yuxuan Liang, Yang Wang
This paper proposes a two-stage framework named ST-PAD for spatio-temporal fluid dynamics modeling in the field of earth sciences, aiming to achieve high-precision simulation and prediction of fluid dynamics through spatio-temporal physics awareness and parameter diffusion guidance.
no code implementations • 16 Aug 2020 • Hao Wang, Jingrong Chen, Xinchen Wan, Han Tian, Jiacheng Xia, Gaoxiong Zeng, Weiyan Wang, Kai Chen, Wei Bai, Junchen Jiang
Communication overhead poses an important obstacle to distributed DNN training and draws increasing attention in recent years.
no code implementations • 7 Jul 2020 • Weiyan Wang, Cengguang Zhang, Liu Yang, Kai Chen, Kun Tan
However, due to the global synchronization nature, its performance can be significantly influenced by network bottlenecks caused by either static topology heterogeneity or dynamic bandwidth contentions.
no code implementations • 30 Dec 2019 • Qinghe Jing, Weiyan Wang, Junxue Zhang, Han Tian, Kai Chen
The scarcity of data and isolated data islands encourage different organizations to share data with each other to train machine learning models.
no code implementations • 10 Nov 2017 • Weiyan Wang, Yuxiang Wu, Yu Zhang, Zhongqi Lu, Kaixiang Mo, Qiang Yang
Then the built user model is used as a leverage to train the agent model by deep reinforcement learning.