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of 502
pro vyhledávání: '"Hwang, Kai"'
Graph classification benchmarks, vital for assessing and developing graph neural networks (GNNs), have recently been scrutinized, as simple methods like MLPs have demonstrated comparable performance. This leads to an important question: Do these benc
Externí odkaz:
http://arxiv.org/abs/2407.04999
Traffic flow forecasting is a highly challenging task due to the dynamic spatial-temporal road conditions. Graph neural networks (GNN) has been widely applied in this task. However, most of these GNNs ignore the effects of time-varying road condition
Externí odkaz:
http://arxiv.org/abs/2307.05517
Spark SQL has been widely deployed in industry but it is challenging to tune its performance. Recent studies try to employ machine learning (ML) to solve this problem, but suffer from two drawbacks. First, it takes a long time (high overhead) to coll
Externí odkaz:
http://arxiv.org/abs/2203.14889
Publikováno v:
In Journal of Cardiothoracic and Vascular Anesthesia March 2024 38(3):802-819
Future advances in deep learning and its impact on the development of artificial intelligence (AI) in all fields depends heavily on data size and computational power. Sacrificing massive computing resources in exchange for better precision rates of t
Externí odkaz:
http://arxiv.org/abs/2007.10878
In a hierarchically-structured cloud/edge/device computing environment, workload allocation can greatly affect the overall system performance. This paper deals with AI-oriented medical workload generated in emergency rooms (ER) or intensive care unit
Externí odkaz:
http://arxiv.org/abs/2002.03493
Akademický článek
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Autor:
Hao, Tianshu, Huang, Yunyou, Wen, Xu, Gao, Wanling, Zhang, Fan, Zheng, Chen, Wang, Lei, Ye, Hainan, Hwang, Kai, Ren, Zujie, Zhan, Jianfeng
In edge computing scenarios, the distribution of data and collaboration of workloads on different layers are serious concerns for performance, privacy, and security issues. So for edge computing benchmarking, we must take an end-to-end view, consider
Externí odkaz:
http://arxiv.org/abs/1908.01924