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Multi-fidelity deep neural networks for adaptive inference in the internet of multimedia things.

, , , , , , , and . Future Gener. Comput. Syst., (2019)

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Multi-fidelity deep neural networks for adaptive inference in the internet of multimedia things., , , , , , , and . Future Gener. Comput. Syst., (2019)Quality of experience driven control of interactive media stream parameters., , , , , , and . IM, page 1282-1287. IEEE, (2013)DLNA interworking for virtualized set-top-boxes., , , , and . EuroITV, page 259-262. ACM, (2010)MobiThin management framework: design and evaluation., , , , , , , , , and . ADAMUS@ICPS, page 25-30. ACM, (2009)Multi-fidelity matryoshka neural networks for constrained IoT devices., , , , , , and . IJCNN, page 1305-1309. IEEE, (2016)Sensor fusion for robot control through deep reinforcement learning., , , , , and . IROS, page 2365-2370. IEEE, (2017)Towards a multimedia remote viewer for mobile thin clients., , , , , , , and . Multimedia on Mobile Devices / Multimedia Content Access: Algorithms and Systems, volume 7881 of SPIE Proceedings, page 788102. SPIE, (2011)Distributed Neural Networks for Internet of Things: The Big-Little Approach., , , , , , and . IoT 360 (2), volume 170 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 484-492. (2015)Middleware Platform for Distributed Applications Incorporating Robots, Sensors and the Cloud., , , , , , and . CloudNet, page 218-223. IEEE, (2016)Quantifying Uncertainty of Deep Neural Networks in Skin Lesion Classification., , , , , , , , and . UNSURE/CLIP@MICCAI, volume 11840 of Lecture Notes in Computer Science, page 52-61. Springer, (2019)