no code implementations • 12 Jul 2023 • Yuxuan Chen, Rongpeng Li, Zhifeng Zhao, Chenghui Peng, Jianjun Wu, Ekram Hossain, Honggang Zhang
Towards personalized generative services, a collaborative cloud-edge methodology is promising, as it facilitates the effective orchestration of heterogeneous distributed communication and computing resources.
no code implementations • 1 Jun 2023 • Xingfu Yi, Rongpeng Li, Chenghui Peng, Fei Wang, Jianjun Wu, Zhifeng Zhao
The rapid development of artificial intelligence (AI) over massive applications including Internet-of-things on cellular network raises the concern of technical challenges such as privacy, heterogeneity and resource efficiency.
no code implementations • 29 Jan 2023 • Jianhang Zhu, Rongpeng Li, Xianfu Chen, Shiwen Mao, Jianjun Wu, Zhifeng Zhao
On top of that, we customize its temporal and structural learning modules to further boost the prediction performance.
no code implementations • 18 Aug 2022 • Jianhang Zhu, Rongpeng Li, Guoru Ding, Chan Wang, Jianjun Wu, Zhifeng Zhao, Honggang Zhang
In this paper, to maximize the cache hit rate, we leverage an effective dynamic graph neural network (DGNN) to jointly learn the structural and temporal patterns embedded in the bipartite graph.
no code implementations • 16 Oct 2021 • Kun Lu, Qingyang Zhou, Rongpeng Li, Zhifeng Zhao, Xianfu Chen, Jianjun Wu, Honggang Zhang
Modern communications are usually designed to pursue a higher bit-level precision and fewer bits while transmitting a message.
no code implementations • 16 Aug 2019 • Peng Chen, Tong Jia, Pengfei Wu, Jianjun Wu, Dongyue Chen
Most existing person re-identification (ReID) methods have good feature representations to distinguish pedestrians with deep convolutional neural network (CNN) and metric learning methods.