no code implementations • 21 May 2023 • Lei Shen, Shuai Yu, Xiaoyu Shen
Cross-lingual transfer is important for developing high-quality chatbots in multiple languages due to the strongly imbalanced distribution of language resources.
no code implementations • 2 Dec 2022 • Qingze Fang, Zhiwei Zhai, Shuai Yu, Qiong Wu, Xiaowen Gong, Xu Chen
The space-air-ground integrated network (SAGIN), one of the key technologies for next-generation mobile communication systems, can facilitate data transmission for users all over the world, especially in some remote areas where vast amounts of informative data are collected by Internet of remote things (IoRT) devices to support various data-driven artificial intelligence (AI) services.
no code implementations • 31 Oct 2022 • Liekang Zeng, Chongyu Yang, Peng Huang, Zhi Zhou, Shuai Yu, Xu Chen
Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques.
1 code implementation • 2 Feb 2022 • Ke Chen, Shuai Yu, Cheng-i Wang, Wei Li, Taylor Berg-Kirkpatrick, Shlomo Dubnov
In this paper, we propose TONet, a plug-and-play model that improves both tone and octave perceptions by leveraging a novel input representation and a novel network architecture.
no code implementations • 25 Dec 2021 • Peng Huang, Liekang Zeng, Xu Chen, Ke Luo, Zhi Zhou, Shuai Yu
With the wide penetration of smart robots in multifarious fields, Simultaneous Localization and Mapping (SLAM) technique in robotics has attracted growing attention in the community.
no code implementations • 26 Feb 2021 • Shuai Yu, Jianyang Xie, Jinkui Hao, Yalin Zheng, Jiong Zhang, Yan Hu, Jiang Liu, Yitian Zhao
Experimental results demonstrate that our method is effective in the depth prediction and 3D vessel reconstruction for OCTA images.% results may be used to guide subsequent vascular analysis
no code implementations • 15 Jan 2021 • Shuai Yu, Xiaowen Gong, Qian Shi, Xiaofei Wang, Xu Chen
After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled space-air-ground integrated networks (EC-SAGINs) to support various IoV services for the vehicles in remote areas.
no code implementations • 22 Sep 2020 • Shuai Yu, Xu Chen, Zhi Zhou, Xiaowen Gong, Di wu
Ultra-dense edge computing (UDEC) has great potential, especially in the 5G era, but it still faces challenges in its current solutions, such as the lack of: i) efficient utilization of multiple 5G resources (e. g., computation, communication, storage and service resources); ii) low overhead offloading decision making and resource allocation strategies; and iii) privacy and security protection schemes.
no code implementations • 15 Jul 2020 • Xin Tang, Xu Chen, Liekang Zeng, Shuai Yu, Lin Chen
With the assistance of edge servers, user equipments (UEs) are able to run deep neural network (DNN) based AI applications, which are generally resource-hungry and compute-intensive, such that an individual UE can hardly afford by itself in real time.
no code implementations • 9 Apr 2020 • Haowei Chen, Liekang Zeng, Shuai Yu, Xu Chen
In this article, we propose an edge computation offloading framework based on Deep Imitation Learning (DIL) and Knowledge Distillation (KD), which assists end devices to quickly make fine-grained decisions to optimize the delay of computation tasks online.
no code implementations • 26 Feb 2020 • Siqi Luo, Xu Chen, Qiong Wu, Zhi Zhou, Shuai Yu
We further formulate a joint computation and communication resource allocation and edge association problem for device users under HFEL framework to achieve global cost minimization.
Distributed, Parallel, and Cluster Computing
no code implementations • 20 Feb 2019 • Shuai Yu, Yongbo Wang, Min Yang, Baocheng Li, Qiang Qu, Jialie Shen
In this paper, we develop a neural attentive interpretable recommendation system, named NAIRS.
1 code implementation • 7 Mar 2018 • Wenyu Du, Shuai Yu, Min Yang, Qiang Qu, Jia Zhu
Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network.