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pro vyhledávání: '"Tripp, Charles"'
Autor:
Tripp, Charles Edison, Perr-Sauer, Jordan, Gafur, Jamil, Nag, Amabarish, Purkayastha, Avi, Zisman, Sagi, Bensen, Erik A.
Addressing the so-called ``Red-AI'' trend of rising energy consumption by large-scale neural networks, this study investigates the actual energy consumption, as measured by node-level watt-meters, of training various fully connected neural network ar
Externí odkaz:
http://arxiv.org/abs/2403.08151
We present an empirical dataset surveying the deep learning phenomenon on fully-connected feed-forward multilayer perceptron neural networks. The dataset, which is now freely available online, records the per-epoch training and generalization perform
Externí odkaz:
http://arxiv.org/abs/2207.12547
Autor:
Skordilis, Erotokritos, Hou, Yi, Tripp, Charles, Moniot, Matthew, Graf, Peter, Biagioni, David
Mobility on demand (MoD) systems show great promise in realizing flexible and efficient urban transportation. However, significant technical challenges arise from operational decision making associated with MoD vehicle dispatch and fleet rebalancing.
Externí odkaz:
http://arxiv.org/abs/2105.13284
Akademický článek
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