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pro vyhledávání: '"Palaniswami, Marimuthu"'
Contrastive Learning and Masked Image Modelling have demonstrated exceptional performance on self-supervised representation learning, where Momentum Contrast (i.e., MoCo) and Masked AutoEncoder (i.e., MAE) are the state-of-the-art, respectively. In t
Externí odkaz:
http://arxiv.org/abs/2302.02089
Masked image modelling (e.g., Masked AutoEncoder) and contrastive learning (e.g., Momentum Contrast) have shown impressive performance on unsupervised visual representation learning. This work presents Masked Contrastive Representation Learning (MACR
Externí odkaz:
http://arxiv.org/abs/2211.06012
Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions
Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. To fully utilize the potential of this computing parad
Externí odkaz:
http://arxiv.org/abs/2204.12580
Autor:
Roy, Dibbendu, Rao, Aravinda S., Alpcan, Tansu, Wick, Akilan, Das, Goutam, Palaniswami, Marimuthu
End-to-end (E2E) quality of experience (QoE) for 6G applications depends on the synchronous allocation of networking and computing resources, also known as slicing. However, the relationship between the resources and the E2E QoE outcomes is typically
Externí odkaz:
http://arxiv.org/abs/2201.05187
Emerging applications such as Augmented Reality, the Internet of Vehicles and Remote Surgery require both computing and networking functions working in harmony. The End-to-end (E2E) quality of experience (QoE) for these applications depends on the sy
Externí odkaz:
http://arxiv.org/abs/2201.05184
Autor:
Mendis, Lochana1 (AUTHOR) lochana.mendis@ieee.org, Palaniswami, Marimuthu1 (AUTHOR), Keenan, Emerson1,2 (AUTHOR), Brownfoot, Fiona2 (AUTHOR)
Publikováno v:
Scientific Reports. 6/1/2024, Vol. 14 Issue 1, p1-15. 15p.
Fog/Edge computing is a novel computing paradigm supporting resource-constrained Internet of Things (IoT) devices by the placement of their tasks on the edge and/or cloud servers. Recently, several Deep Reinforcement Learning (DRL)-based placement te
Externí odkaz:
http://arxiv.org/abs/2110.12415
Emerging real-time applications such as those classified under ultra-reliable low latency (uRLLC) generate bursty traffic and have strict Quality of Service (QoS) requirements. Passive Optical Network (PON) is a popular access network technology, whi
Externí odkaz:
http://arxiv.org/abs/2109.02186
Fog/Edge computing model allows harnessing of resources in the proximity of the Internet of Things (IoT) devices to support various types of real-time IoT applications. However, due to the mobility of users and a wide range of IoT applications with d
Externí odkaz:
http://arxiv.org/abs/2108.02328
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