2 code implementations • 11 Nov 2024 • Chengyu Yang, Chengjun Liu
To increase rosacea awareness, automatic rosacea detection methods using deep learning and explainable statistical approaches are presented in this paper.
no code implementations • 23 May 2024 • Shuaipeng Li, Penghao Zhao, Hailin Zhang, Xingwu Sun, Hao Wu, Dian Jiao, Weiyan Wang, Chengjun Liu, Zheng Fang, Jinbao Xue, Yangyu Tao, Bin Cui, Di Wang
First, we raise the scaling law between batch sizes and optimal learning rates in the sign of gradient case, in which we prove that the optimal learning rate first rises and then falls as the batch size increases.
1 code implementation • 26 Aug 2022 • Hadi Ghahremannezhad, Chengjun Liu, Hang Shi
The trained models are tested against unseen data in order to evaluate the performance of the applied method.
1 code implementation • 12 Aug 2022 • Hadi Ghahremannezhad, Hang Shi, Chengjun Liu
This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications.
1 code implementation • 10 Nov 2021 • Xiangru Lian, Binhang Yuan, XueFeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen yang, Ce Zhang, Ji Liu
Specifically, in order to ensure both the training efficiency and the training accuracy, we design a novel hybrid training algorithm, where the embedding layer and the dense neural network are handled by different synchronization mechanisms; then we build a system called Persia (short for parallel recommendation training system with hybrid acceleration) to support this hybrid training algorithm.
no code implementations • 7 Sep 2021 • Guanxiong Liu, Hang Shi, Abbas Kiani, Abdallah Khreishah, Jo Young Lee, Nirwan Ansari, Chengjun Liu, Mustafa Yousef
In this paper, we focus on two common traffic monitoring tasks, congestion detection, and speed detection, and propose a two-tier edge computing based model that takes into account of both the limited computing capability in cloudlets and the unstable network condition to the TMC.
1 code implementation • 3 Jul 2021 • Shaoduo Gan, Xiangru Lian, Rui Wang, Jianbin Chang, Chengjun Liu, Hongmei Shi, Shengzhuo Zhang, Xianghong Li, Tengxu Sun, Jiawei Jiang, Binhang Yuan, Sen yang, Ji Liu, Ce Zhang
Recent years have witnessed a growing list of systems for distributed data-parallel training.
no code implementations • CVPR 2015 • Qing-Feng Liu, Chengjun Liu
And then the locally linear KNN model based classifier (LLKNNC), which shows its connection to the Bayes decision rule for minimum error in the view of kernel density estimation, is proposed for classification.