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of 9
pro vyhledávání: '"Lyu, Chengfei"'
Many large vision models have been deployed on the cloud for real-time services. Meanwhile, fresh samples are continuously generated on the served mobile device. How to leverage the device-side samples to improve the cloud-side large model becomes a
Externí odkaz:
http://arxiv.org/abs/2303.10361
One-Time Model Adaptation to Heterogeneous Clients: An Intra-Client and Inter-Image Attention Design
The mainstream workflow of image recognition applications is first training one global model on the cloud for a wide range of classes and then serving numerous clients, each with heterogeneous images from a small subset of classes to be recognized. F
Externí odkaz:
http://arxiv.org/abs/2211.06276
To meet the practical requirements of low latency, low cost, and good privacy in online intelligent services, more and more deep learning models are offloaded from the cloud to mobile devices. To further deal with cross-device data heterogeneity, the
Externí odkaz:
http://arxiv.org/abs/2211.01163
Publikováno v:
中国工程科学, Vol 26, Iss 1, Pp 127-138 (2024)
Device-cloud collaborative intelligent computing, an emergent result of the development in big data, cloud computing, and edge computing, offers significant improvements in data utilization while protecting user privacy. This approach synergizes the
Externí odkaz:
https://doaj.org/article/4c9027889cdc4384b5dfdc1c296e4e13
Autor:
Gu, Renjie, Niu, Chaoyue, Yan, Yikai, Wu, Fan, Tang, Shaojie, Jia, Rongfeng, Lyu, Chengfei, Chen, Guihai
Data heterogeneity is an intrinsic property of recommender systems, making models trained over the global data on the cloud, which is the mainstream in industry, non-optimal to each individual user's local data distribution. To deal with data heterog
Externí odkaz:
http://arxiv.org/abs/2201.10382
Publikováno v:
ACM Comput. Surv. 54, 1 (2021), 6:1 - 6:36 (2021)
In recent years, mobile devices have gained increasing development with stronger computation capability and larger storage space. Some of the computation-intensive machine learning tasks can now be run on mobile devices. To exploit the resources avai
Externí odkaz:
http://arxiv.org/abs/1909.08329
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Autor:
Yan, Yikai, Niu, Chaoyue, Ding, Yucheng, Zheng, Zhenzhe, Tang, Shaojie, Li, Qinya, Wu, Fan, Lyu, Chengfei, Feng, Yanghe, Chen, Guihai
Publikováno v:
INFORMS Journal on Computing; Jan/Feb2024, Vol. 36 Issue 1, p185-202, 18p
Akademický článek
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