Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Zhu, Zhongwen"'
Parameter-Efficient Fine-Tuning (PEFT) has risen as an innovative training strategy that updates only a select few model parameters, significantly lowering both computational and memory demands. PEFT also helps to decrease data transfer in federated
Externí odkaz:
http://arxiv.org/abs/2409.02346
The emergence of 5G technology marks a significant milestone in developing telecommunication networks, enabling exciting new applications such as augmented reality and self-driving vehicles. However, these improvements bring an increased management c
Externí odkaz:
http://arxiv.org/abs/2406.15638
We propose a secure inference protocol for a distributed setting involving a single server node and multiple client nodes. We assume that the observed data vector is partitioned across multiple client nodes while the deep learning model is located at
Externí odkaz:
http://arxiv.org/abs/2405.03775
The rise of 5G deployments has created the environment for many emerging technologies to flourish. Self-driving vehicles, Augmented and Virtual Reality, and remote operations are examples of applications that leverage 5G networks' support for extreme
Externí odkaz:
http://arxiv.org/abs/2404.10643
Semi- and weakly-supervised learning have recently attracted considerable attention in the object detection literature since they can alleviate the cost of annotation needed to successfully train deep learning models. State-of-art approaches for semi
Externí odkaz:
http://arxiv.org/abs/2204.00147
Autor:
Zhou, Dinghua, Zhu, Zhongwen, Li, Cheng, Jiang, Weihai, Ma, Yan, Lu, Jianwei, Li, Shuhua, Wang, Weizhi
Publikováno v:
In Journal of Energy Storage 15 September 2024 98 Part A
Autor:
Jitani, Anirudha, Mahajan, Aditya, Zhu, Zhongwen, Abou-zeid, Hatem, Fapi, Emmanuel T., Purmehdi, Hakimeh
Mobile Edge Computing (MEC) refers to the concept of placing computational capability and applications at the edge of the network, providing benefits such as reduced latency in handling client requests, reduced network congestion, and improved perfor
Externí odkaz:
http://arxiv.org/abs/2107.01025
Publikováno v:
In International Journal of Hydrogen Energy 29 February 2024 57:990-999
Autor:
Zeng, Tao, Zhang, Caizhi, Zhang, Yanyi, Deng, Chenghao, Hao, Dong, Zhu, Zhongwen, Ran, Hongxu, Cao, Dongpu
Publikováno v:
In Energy 15 July 2021 227
Autor:
Huang, Denggao *, Wang, Yuehui, Zhao, Jing, Wang, Xu, Zhu, Zhongwen, Wang, Tong, Zhou, Yilu, Jin, Peng, Li, Cheng
Publikováno v:
In IFAC PapersOnLine 2020 53(6) Supplement:13-18