an architectureis designedfrom the ground up for Deep Learning ... with the ability to handle large - scale neural networks

which has used super pixel delimiting masks as a form of data augmentationleadingto reasonable results with respect to ten Convolutional Neural Network architectures

AutoML has been usedhow ... to discovernew competitive neural network architectures

how AutoML has been usedto discovernew competitive neural network architectures

that allowsto designdeep learning neural networks on C # applications

how differentinfluencethe vulnerability of deep neural networks

for real - time object detection(passive) can be designedvery small deep neural network architectures

The VGG networkgreatly influencedthe design of deep convolutional neural networks

some applicationsspecially designedwith the help of deep learning artificial neural networks

of a set of neurons(passive) are composedArchitecture of a Neural Network Artificial neural networks

The authorsdesignedan accelerator architecture for large - scale neural networks

the projection of signals ( a.k.a . pursuit ) to the ML - CSC modelleadsto various deep convolutional neural network architectures

by the authors(passive) designed bythe deep neural network architecture

to predict the contingency ranking of a system(passive) are designedSuitable Artificial Neural Network ( ANN ) architectures

Their ability to model complex , non - linear relationships that exist in dataledto novel neural network architectures

its abilityto ... discoverpromising architectures of deep neural networks

chipsare ... designedfor DNN ( Deep Neural Network ) workloads

to provide human expertise to machines(passive) were designedSeveral neural network architectures

computers ... ableto automatically discovernovel neural network architectures

Three major driverscausedthe breakthrough of ( deep ) neural networks

techniques ableto automatically discoverefficient neural network architectures

Improvement in hardware and softwarealso contributedto deep learning from using neural networks

Three major drivers ... the availability of huge amounts of training data , powerful computational infrastructure , and advances in academiacausedthe breakthrough of ( deep ) neural networks

how evolutionary algorithms can be usedto ... discoverstate - of - the - art neural network architectures

in the early 1980s(passive) were inventedDeep convolutional neural networks

to evaluate , evolve , and optimize neural networks for unique datasets(passive) is designedMultinode Evolutionary Neural Networks for Deep Learning

to evaluate , evolve , and optimize neural networks for unique(passive) is designedMultinode Evolutionary Neural Networks for Deep Learning

our AutoML effortsto designneural network architectures and optimizers

o use AIto designneural network architectures

an algorithm ... ableto discoverarchitectures of a neural network

that allowsto designdeep learning neural networks

Neural network architecture search ( NAS ) has been widely usedto designneural network architectures

to work just like we do(passive) are designedDeep learning neural networks

ENAS uses a Reinforcement learning - based controllerto discoverneural network architectures

most often(passive) are ... designedNeural network architectures

In ENAS , a controller learnsto discoverneural network architectures

with images in mind where this is not the case(passive) are often designedDeep learning architectures