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Neural networks develop into bigger and larger and use up to billions of parameters. Researchers commence to quantify the work to train these large-scale models in $$$ amounts on cloud computing platforms and even in tons of carbon emissions. The common understanding made use of to be that overparametrized networks have much more capacity but are also a lot more prone to overfit the education 파워볼분석기 data. Recent research have shown that overparametrization, in truth, acts as a regularizer and lead to enhanced generalization efficiency that no other regularization method may achieve1. A important limitation of the paper is that it only runs on somewhat compact datasets such as MNIST and CIFAR-ten, and not on bigger datasets like ImageNet.
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Our system prunes redundant connections working with a 3-step system. Very first, we train the network to understand which connections are essential. Ultimately, we retrain the network to fine tune the weights of the remaining connections. On the ImageNet dataset, our system reduced the quantity of parameters of AlexNet by a element of 9x, from 61 million to 6.7 million, devoid of incurring accuracy loss. Comparable experiments with VGG16 found that the network as a complete can be reduced six.8x just by pruning the fully-connected layers, once more with no loss of accuracy.
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