Zobrazeno 1 - 10
of 168
pro vyhledávání: '"Zhou, Zimu"'
Publikováno v:
The 22th ACM Conference on Embedded Networked Sensor Systems, 2024
On-device adapting to continual, unpredictable domain shifts is essential for mobile applications like autonomous driving and augmented reality to deliver seamless user experiences in evolving environments. Test-time adaptation (TTA) emerges as a pro
Externí odkaz:
http://arxiv.org/abs/2410.08256
Autor:
Liu, Sicong, Zhou, Wentao, Zhou, Zimu, Guo, Bin, Wang, Minfan, Fang, Cheng, Lin, Zheng, Yu, Zhiwen
There is a growing demand to deploy computation-intensive deep learning (DL) models on resource-constrained mobile devices for real-time intelligent applications. Equipped with a variety of processing units such as CPUs, GPUs, and NPUs, the mobile de
Externí odkaz:
http://arxiv.org/abs/2405.01851
Executing deep neural networks (DNNs) on edge artificial intelligence (AI) devices enables various autonomous mobile computing applications. However, the memory budget of edge AI devices restricts the number and complexity of DNNs allowed in such app
Externí odkaz:
http://arxiv.org/abs/2401.16757
The rise of mobile devices with abundant sensory data and local computing capabilities has driven the trend of federated learning (FL) on these devices. And personalized FL (PFL) emerges to train specific deep models for each mobile device to address
Externí odkaz:
http://arxiv.org/abs/2401.15960
Federated Trajectory Matching (FTM) is gaining increasing importance in big trajectory data analytics, supporting diverse applications such as public health, law enforcement, and emergency response. FTM retrieves trajectories that match with a query
Externí odkaz:
http://arxiv.org/abs/2312.12012
The emerging field of artificial intelligence of things (AIoT, AI+IoT) is driven by the widespread use of intelligent infrastructures and the impressive success of deep learning (DL). With the deployment of DL on various intelligent infrastructures f
Externí odkaz:
http://arxiv.org/abs/2309.15467
Spatial-temporal graph models are prevailing for abstracting and modelling spatial and temporal dependencies. In this work, we ask the following question: whether and to what extent can we localise spatial-temporal graph models? We limit our scope to
Externí odkaz:
http://arxiv.org/abs/2306.06930
Autor:
Wei, Shuyue, Tong, Yongxin, Zhou, Zimu, Liu, Qiaoyang, Zhang, Lulu, Zeng, Yuxiang, Ye, Jieping
Online real estate platforms are gaining increasing popularity, where a central issue is to match brokers with clients for potential housing transactions. Mainstream platforms match brokers via top-k recommendation. Yet we observe through extensive d
Externí odkaz:
http://arxiv.org/abs/2303.03024
The insurance industry is shifting their sales mode from offline to online, in expectation to reach massive potential customers in the digitization era. Due to the complexity and the nature of insurance products, a cost-effective online sales solutio
Externí odkaz:
http://arxiv.org/abs/2302.06470
The ubiquity of camera-embedded devices and the advances in deep learning have stimulated various intelligent mobile video applications. These applications often demand on-device processing of video streams to deliver real-time, high-quality services
Externí odkaz:
http://arxiv.org/abs/2211.16135