標(biāo)題: Titlebook: Edge Intelligence in the Making; Optimization, Deep L Sen Lin,Zhi Zhou,Junshan Zhang Book 2021 Springer Nature Switzerland AG 2021 [打印本頁(yè)] 作者: fundoplication 時(shí)間: 2025-3-21 17:37
書目名稱Edge Intelligence in the Making影響因子(影響力)
書目名稱Edge Intelligence in the Making影響因子(影響力)學(xué)科排名
書目名稱Edge Intelligence in the Making網(wǎng)絡(luò)公開度
書目名稱Edge Intelligence in the Making網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Edge Intelligence in the Making被引頻次
書目名稱Edge Intelligence in the Making被引頻次學(xué)科排名
書目名稱Edge Intelligence in the Making年度引用
書目名稱Edge Intelligence in the Making年度引用學(xué)科排名
書目名稱Edge Intelligence in the Making讀者反饋
書目名稱Edge Intelligence in the Making讀者反饋學(xué)科排名
作者: conifer 時(shí)間: 2025-3-21 22:34 作者: Spinal-Fusion 時(shí)間: 2025-3-22 03:22 作者: locus-ceruleus 時(shí)間: 2025-3-22 07:50
2690-4306 io surveillance, billions of mobile and IoT devices are being connected to the Internet, generating zillions of bytes of data at the network edge.. Driven by this trend, there is an urgent need to push the frontiers of artificial intelligence (AI) to the network edge to fully unleash the potential o作者: 粉筆 時(shí)間: 2025-3-22 10:41
https://doi.org/10.1007/978-981-99-8857-0dation, video surveillance, and smart home appliances, which have quickly ascended to the spotlight and gained enormous popularity. It is widely recognized that these intelligent applications are significantly enriching people’s lifes, improving human productivity and enhancing social efficiency.作者: Paraplegia 時(shí)間: 2025-3-22 15:27 作者: Paraplegia 時(shí)間: 2025-3-22 20:00 作者: DIKE 時(shí)間: 2025-3-22 23:46
Edge Intelligence via Federated Meta-Learning,he knowledge transferred from other edge nodes or the cloud. In this chapter, we focus on collaborative learning across edge nodes, and turn our attention to collaborative learning between the edge and the cloud in next chapter, aiming to fully leverage the potentially valuable knowledge transfer from the cloud.作者: 修改 時(shí)間: 2025-3-23 05:16
,China’s Basic Foreign Policy Objectives,he knowledge transferred from other edge nodes or the cloud. In this chapter, we focus on collaborative learning across edge nodes, and turn our attention to collaborative learning between the edge and the cloud in next chapter, aiming to fully leverage the potentially valuable knowledge transfer from the cloud.作者: gain631 時(shí)間: 2025-3-23 05:42 作者: cataract 時(shí)間: 2025-3-23 11:12 作者: inculpate 時(shí)間: 2025-3-23 15:34 作者: Common-Migraine 時(shí)間: 2025-3-23 18:22
Applications, Marketplaces, and Future Directions of Edge Intelligence,share our view of its applications, marketplaces, and future research directions to conclude this book. Edge intelligence is an emerging interdisciplinary field with many open problems and yet tremendous opportunities, and our study in this monograph touches only the tip of iceberg.作者: 躲債 時(shí)間: 2025-3-24 00:29
https://doi.org/10.1007/978-981-99-8857-0 and big data, deep learning [Lecun et al., 2015]—the most dazzling sector of AI—has made substantial breakthroughs in a wide spectrum of fields, ranging from computer vision, speech recognition, and natural language processing to chess playing (e.g., AlphaGo) and robotics [Deng et al., 2014]. Benef作者: micturition 時(shí)間: 2025-3-24 05:44
,China’s Basic Foreign Policy Objectives,cally require high computational power that greatly outweighs the capacity of resource- and energy-constrained IoT devices, it is highly challenging for a single edge node alone to achieve real-time edge intelligence, which points to the need of collaborative learning that is capable of leveraging t作者: nitroglycerin 時(shí)間: 2025-3-24 10:28 作者: conformity 時(shí)間: 2025-3-24 13:24
Lorenzo Riccardi,Giorgio Riccardishare our view of its applications, marketplaces, and future research directions to conclude this book. Edge intelligence is an emerging interdisciplinary field with many open problems and yet tremendous opportunities, and our study in this monograph touches only the tip of iceberg.作者: facilitate 時(shí)間: 2025-3-24 17:16
Synthesis Lectures on Learning, Networks, and Algorithmshttp://image.papertrans.cn/e/image/302240.jpg作者: goodwill 時(shí)間: 2025-3-24 21:17 作者: 衣服 時(shí)間: 2025-3-25 02:21
Warum nicht in den Osten blicken?,ity edge intelligence service deployment. In this chapter, we discuss the DNN model inference at the edge, including the architectures, key performance indicators, enabling techniques, and existing systems and frameworks.作者: 萬靈丹 時(shí)間: 2025-3-25 05:01 作者: Paraplegia 時(shí)間: 2025-3-25 07:42 作者: 元音 時(shí)間: 2025-3-25 12:40
China im Blickpunkt des 21. JahrhundertsIn this chapter, we present an execution paradigm of hybrid parallelism to accelerate the DNN model training process under the hierarchical mobile-edge-cloud architecture.作者: Confound 時(shí)間: 2025-3-25 16:41 作者: beta-cells 時(shí)間: 2025-3-25 21:52 作者: dermatomyositis 時(shí)間: 2025-3-26 03:17
Hierarchical Mobile-Edge-Cloud Model Training with Hybrid Parallelism,In this chapter, we present an execution paradigm of hybrid parallelism to accelerate the DNN model training process under the hierarchical mobile-edge-cloud architecture.作者: AUGUR 時(shí)間: 2025-3-26 07:25
On-Demand Accelerating Deep Neural Network Inference via Edge Computing,In this chapter, we study how to accelerate DNN inference under device-edge synergy, by jointly applying the two knobs of DNN model partitioning and right-sizing.作者: 輕快來事 時(shí)間: 2025-3-26 10:12
978-3-031-01252-5Springer Nature Switzerland AG 2021作者: OTTER 時(shí)間: 2025-3-26 12:40 作者: 原來 時(shí)間: 2025-3-26 17:17 作者: exhilaration 時(shí)間: 2025-3-26 21:31
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