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pro vyhledávání: '"Huang, Tianchi"'
Quality of Experience~(QoE)-driven adaptive bitrate (ABR) algorithms are typically optimized using QoE models that are based on the mean opinion score~(MOS), while such principles may not account for user heterogeneity on rating scales, resulting in
Externí odkaz:
http://arxiv.org/abs/2308.04132
Autor:
Gültekin, Kayhan, Nyland, Kristina, Gray, Nichole, Fehmer, Greg, Huang, Tianchi, Sparkman, Matthew, Reines, Amy E., Greene, Jenny E., Cackett, Edward M., Baldassare, Vivienne
We present new 5 GHz VLA observations of a sample of 8 active intermediate-mass black holes with masses $10^{4.9} < M < 10^{6.1}\ M_{\odot}$ found in galaxies with stellar masses $M_{*} < 3 \times 10^{9}\ M_{\odot}$. We detected 5 of the 8 sources at
Externí odkaz:
http://arxiv.org/abs/2209.09890
Video transmission services adopt adaptive algorithms to ensure users' demands. Existing techniques are often optimized and evaluated by a function that linearly combines several weighted metrics. Nevertheless, we observe that the given function fail
Externí odkaz:
http://arxiv.org/abs/2005.12788
Akademický článek
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Akademický článek
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Publikováno v:
Neural Computation, Volume 31, Issue 11, November 2019, p.2266-2291
Human beings are able to master a variety of knowledge and skills with ongoing learning. By contrast, dramatic performance degradation is observed when new tasks are added to an existing neural network model. This phenomenon, termed as \emph{Catastro
Externí odkaz:
http://arxiv.org/abs/1910.10986
Federated learning (FL) aims to train machine learning models in the decentralized system consisting of an enormous amount of smart edge devices. Federated averaging (FedAvg), the fundamental algorithm in FL settings, proposes on-device training and
Externí odkaz:
http://arxiv.org/abs/1910.08234
Federated learning (FL) enables on-device training over distributed networks consisting of a massive amount of modern smart devices, such as smartphones and IoT (Internet of Things) devices. However, the leading optimization algorithm in such setting
Externí odkaz:
http://arxiv.org/abs/1908.05891
Learning-based Adaptive Bit Rate~(ABR) method, aiming to learn outstanding strategies without any presumptions, has become one of the research hotspots for adaptive streaming. However, it typically suffers from several issues, i.e., low sample effici
Externí odkaz:
http://arxiv.org/abs/1908.02270
Video caching has been a basic network functionality in today's network architectures. Although the abundance of caching replacement algorithms has been proposed recently, these methods all suffer from a key limitation: due to their immature rules, i
Externí odkaz:
http://arxiv.org/abs/1905.06650