no code implementations • 16 Apr 2024 • Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Bo Gao, Tianliu He, Wen Wang
On-device intelligence (ODI) enables artificial intelligence (AI) applications to run on end devices, providing real-time and customized AI inference without relying on remote servers.
no code implementations • 8 Jan 2024 • Yuhan Tang, Zhiyuan Wu, Bo Gao, Tian Wen, Yuwei Wang, Sheng Sun
Federated Distillation (FD) is a novel and promising distributed machine learning paradigm, where knowledge distillation is leveraged to facilitate a more efficient and flexible cross-device knowledge transfer in federated learning.
2 code implementations • 1 Jan 2024 • Zhiyuan Wu, Tianliu He, Sheng Sun, Yuwei Wang, Min Liu, Bo Gao, Xuefeng Jiang
Federated Learning (FL) enables collaborative model training among participants while guaranteeing the privacy of raw data.
1 code implementation • 1 Dec 2023 • Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Bo Gao, Quyang Pan, Tianliu He, Xuefeng Jiang
Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy.
no code implementations • 14 Nov 2023 • Yuwei Wang, Runhan Li, Hao Tan, Xuefeng Jiang, Sheng Sun, Min Liu, Bo Gao, Zhiyuan Wu
By fusing the logits of the two models, the private weak learner can capture the variance of different data, regardless of their category.
1 code implementation • 10 Oct 2023 • Cong Yang, Bipin Indurkhya, John See, Bo Gao, Yan Ke, Zeyd Boukhers, Zhenyu Yang, Marcin Grzegorzek
However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards.
no code implementations • 17 Feb 2023 • Qingxiang Liu, Sheng Sun, Min Liu, Yuwei Wang, Bo Gao
In this paper, we perform the first study of forecasting traffic flow adopting Online Learning (OL) manner in FL framework and then propose a novel prediction method named Online Spatio-Temporal Correlation-based Federated Learning (FedOSTC), aiming to guarantee performance gains regardless of traffic fluctuation.
1 code implementation • 14 Jan 2023 • Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Xuefeng Jiang, Runhan Li, Bo Gao
The increasing demand for intelligent services and privacy protection of mobile and Internet of Things (IoT) devices motivates the wide application of Federated Edge Learning (FEL), in which devices collaboratively train on-device Machine Learning (ML) models without sharing their private data.
1 code implementation • 1 Jan 2023 • Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Quyang Pan, Xuefeng Jiang, Bo Gao
Federated Multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC.
no code implementations • 18 Jun 2021 • Baoming Yan, Lin Wang, Ke Gao, Bo Gao, Xiao Liu, Chao Ban, Jiang Yang, Xiaobo Li
Video affective understanding, which aims to predict the evoked expressions by the video content, is desired for video creation and recommendation.
no code implementations • 29 Jan 2021 • Shisheng Li, Jinhua Hong, Bo Gao, Yung-Chang Lin, Hong En Lim, Xueyi Lu, Jing Wu, Song Liu, Yoshitaka Tateyama, Yoshiki Sakuma, Kazuhito Tsukagoshi, Kazu Suenaga, Takaaki Taniguchi
Alternatively, using highly conductive doped TMDCs will have a profound impact on the contact engineering of 2D electronics.
Materials Science
no code implementations • 28 Sep 2020 • Jie Yang, Jing Xu, Xiao Li, Shi Jin, Bo Gao
As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation mobile communication systems have been conducted.
1 code implementation • 31 Jul 2020 • Bo Gao, M. W. Spratling
Finding a template in a search image is an important task underlying many computer vision applications.
no code implementations • ICCV 2015 • Jianwei Li, Xiaowu Chen, Dongqing Zou, Bo Gao, Wei Teng
In this paper, we propose a novel sparse representation approach called conformal and low-rank sparse representation (CLRSR) for image restoration problems.