no code implementations • 1 Dec 2023 • Dengbo Li, Jieren Cheng, Boyi Liu
Our findings highlight the vital role of server-side offloading in DNN-based camera relocation for autonomous vehicles, and we also discuss the results of data fusion.
no code implementations • 15 Feb 2023 • Wenxuan Tu, Bin Xiao, Xinwang Liu, Sihang Zhou, Zhiping Cai, Jieren Cheng
With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world.
no code implementations • 28 Sep 2022 • Ping Luo, Jieren Cheng, Zhenhao Liu, N. Xiong, Jie Wu
However, the clients' Non-Independent and Identically Distributed (Non-IID) data negatively affect the trained model, and clients with different numbers of local updates may cause significant gaps to the local gradients in each communication round.
no code implementations • 8 Sep 2022 • Hui Wang, Jieren Cheng, Yichen Xu, Sirui Ni, Zaijia Yang, Jiangpeng Li
However, with wide applications of deep learning in robotic arms, there are new challenges such as the allocation of grasping computing power and the growing demand for security.
1 code implementation • 18 Nov 2021 • Zhicheng Zhou, Hailong Chen, Kunhua Li, Fei Hu, Bingjie Yan, Jieren Cheng, Xuyan Wei, Bernie Liu, Xiulai Li, Fuwen Chen, Yongji Sui
How to find a balance between the model performance and the communication cost is a challenge in AFL.
no code implementations • 14 Sep 2021 • Jieren Cheng, Le Liu, Xiangyan Tang, Wenxuan Tu, Boyi Liu, Ke Zhou, Qiaobo Da, Yue Yang
In practice, since the label of the target domain is not available, we use the clustering information of the source domain to assign pseudo labels to the target domain samples, and then according to the source domain data prior knowledge guides those positive features to maximum the inter-class distance between different classes and mimimum the intra-class distance.
1 code implementation • 15 Dec 2020 • Wenxuan Tu, Sihang Zhou, Xinwang Liu, Xifeng Guo, Zhiping Cai, En Zhu, Jieren Cheng
Specifically, in our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder for consensus representation learning.
no code implementations • 25 Jun 2019 • Boyi Liu, Xiangyan Tang, Jieren Cheng, Pengchao Shi
In this paper, we define the traffic data time singularity ratio in the dropout module and propose a combination prediction method based on the improved long short-term memory neural network and time series autoregressive integrated moving average model (SDLSTM-ARIMA), which is derived from the Recurrent Neural Networks (RNN) model.