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This is likely explained by the fact that the loss surface for a network of smaller size probably includes drastically fewer nearby optima, but a far more fleshed-out explanation would have been nice. The authors present an algorithm that can identify 파워볼 a "winning ticket" by pruning the weights with the smallest magnitudes, removing those nodes completely, and then retraining the network once more . Successive iterations make the networks smaller, even though preserving the accuracy of the original larger model.
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It is attainable, via neural network pruning, to remove a lot of these unused connections to make the overall architecture lighter and more rapidly to run on some hardware. The second and fourth rows in Figures 11–14 track the distance and angle metrics as described in Section three under Methodology for our sample networks in the course of instruction. The L2 distance in between a ticket and the corresponding complete network grows immediately at the begin of instruction. Immediately after this initial growth, the L2 distance plateaus for the rest of instruction.

Their experiments involve classic handle problems and Atari games for reinforcement learning, as nicely as LSTMs and Transformers for NLP. They managed to obtain winning tickets for all architectures. This suggests that the LTH phenomenon is not restricted to supervised image classification but might be a common home 파워볼분석기 of deep neural nets. , our winning ticket will always consist of three nonzero weights. As a result, we can prune all but these three weights without harming accuracy. An alternative would be that the input values are passed via the initial layer and summed up in the second layer, which would will need four weights in total.

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