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Cited by 20 publications
(7 citation statements)
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“…The use of artificial intelligence techniques for fault identification has been growing over the years and become a hot topic today, especially for electric power systems [ 23 ]. Deep learning models have been increasingly used to improve the ability to identify faults in an electrical grid [ 24 , 25 , 26 ]. However, as these models have a large number of layers, they require more computational effort, making the choice of the appropriate model a challenge [ 27 ].…”
Section: Related Workmentioning
confidence: 99%
“…The use of artificial intelligence techniques for fault identification has been growing over the years and become a hot topic today, especially for electric power systems [ 23 ]. Deep learning models have been increasingly used to improve the ability to identify faults in an electrical grid [ 24 , 25 , 26 ]. However, as these models have a large number of layers, they require more computational effort, making the choice of the appropriate model a challenge [ 27 ].…”
Section: Related Workmentioning
confidence: 99%
“…The use of artificial intelligence techniques for fault identification has been growing over the years, becoming nowadays a hot topic, especially for the electric power system [23]. Deep learning models have being increasingly used to improve the ability to identify faults in the electrical grid [24][25][26]. However, as these models have a large number of layers, they require more computational effort, making the choice of the appropriate model a challenge [27].…”
Section: Related Workmentioning
confidence: 99%
“…Herein we consider the widely known medium size dataset (MNIST) 4 and compare two similar implementations in order to evaluate the efficiency of the software platforms and libraries. A 2-layers LB-CNN is built with nf1=16 filters and nf2=20 filters.…”
Section: Training Speed Of the Binary Kernels Optimizermentioning
confidence: 99%
“…The results are summarized in Table I . 4 http://yann.lecun.com/exdb/mnist/ Note the impressive increase in performance when using the CUPY library for ELM, with a reduction of the training time of more than 60 times ! Moreover, the implementation of the convolution layers are much, much faster using the CHAINER framework, with speed-ups of 1800 times !…”
Section: Training Speed Of the Binary Kernels Optimizermentioning
confidence: 99%
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