Zobrazeno 1 - 10
of 94
pro vyhledávání: '"Ouyang, Ye"'
Autor:
Kong, Rui, Li, Yuanchun, Feng, Qingtian, Wang, Weijun, Ye, Xiaozhou, Ouyang, Ye, Kong, Linghe, Liu, Yunxin
Mixture of experts (MoE) is a popular technique to improve capacity of Large Language Models (LLMs) with conditionally-activated parallel experts. However, serving MoE models on memory-constrained devices is challenging due to the large parameter siz
Externí odkaz:
http://arxiv.org/abs/2308.15030
Autor:
Ouyang, Ye, Zhang, Yaqin, Ye, Xiaozhou, Liu, Yunxin, Song, Yong, Liu, Yang, Bian, Sen, Liu, Zhiyong
In the global craze of GPT, people have deeply realized that AI, as a transformative technology and key force in economic and social development, will bring great leaps and breakthroughs to the global industry and profoundly influence the future worl
Externí odkaz:
http://arxiv.org/abs/2307.11449
Autor:
Ouyang, Ye, Zhang, Yaqin, Wang, Peng, Liu, Yunxin, Qiao, Wen, Zhu, Jun, Liu, Yang, Zhang, Feng, Wang, Shuling, Wang, Xidong
6G is the next-generation intelligent and integrated digital information infrastructure, characterized by ubiquitous interconnection, native intelligence, multi-dimensional perception, global coverage, green and low-carbon, native network security, e
Externí odkaz:
http://arxiv.org/abs/2307.10004
Autor:
Ouyang, Ye, Zhang, Yaqin, Ye, Xiaozhou, Liu, Yunxin, Wang, Xidong, Sun, Jie, Liu, Yang, Wang, Shoufeng, Bian, Sen, Li, Yun
6G is the next-generation intelligent and integrated digital information infrastructure, characterized by ubiquitous interconnection, native intelligence, multi-dimensional perception, global coverage, green and low-carbon, native network security, e
Externí odkaz:
http://arxiv.org/abs/2307.09045
Autor:
Wen, Hao, Li, Yuanchun, Zhang, Zunshuai, Jiang, Shiqi, Ye, Xiaozhou, Ouyang, Ye, Zhang, Ya-Qin, Liu, Yunxin
Deep learning models are increasingly deployed to edge devices for real-time applications. To ensure stable service quality across diverse edge environments, it is highly desirable to generate tailored model architectures for different conditions. Ho
Externí odkaz:
http://arxiv.org/abs/2303.07129
Autor:
Liu, Yang, Kang, Yan, Zou, Tianyuan, Pu, Yanhong, He, Yuanqin, Ye, Xiaozhou, Ouyang, Ye, Zhang, Ya-Qin, Yang, Qiang
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering 2024
Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models without exposing their raw data or model parameters. Motivated by the r
Externí odkaz:
http://arxiv.org/abs/2211.12814
As computing power is becoming the core productivity of the digital economy era, the concept of Computing and Network Convergence (CNC), under which network and computing resources can be dynamically scheduled and allocated according to users' needs,
Externí odkaz:
http://arxiv.org/abs/2209.10753
In the traditional distributed machine learning scenario, the user's private data is transmitted between clients and a central server, which results in significant potential privacy risks. In order to balance the issues of data privacy and joint trai
Externí odkaz:
http://arxiv.org/abs/2208.01200
Publikováno v:
ITU Journal on Future and Evolving Technologies, Volume 3 (2022), Issue 3 - AI and machine learning solutions in 5G and future networks, Pages 21-29
With the development of 4G/5G, the rapid growth of traffic has caused a large number of cell indicators to exceed the warning threshold, and network quality has deteriorated. It is necessary for operators to solve the congestion in advance and effect
Externí odkaz:
http://arxiv.org/abs/2209.05989