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Distributed optimization for deep learning with gossip exchange
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DOI：10.1016/j.neucom.2018.11.002.png)
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摘要
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We address the issue of speeding up the training of convolutional neural networks by studying a distributed method adapted to stochastic gradient descent. Our parallel optimization setup uses several threads, each applying individual gradient descents on a local variable. We propose a new way of sharing information between different threads based on gossip algorithms that show good consensus convergence properties. Our method called GoSGD has the advantage to be fully asynchronous and decentralized. (C) 2018 Published by Elsevier B.V.
Keyword:
Optimization
Distributed gradient descent
Gossip
Deep
Learning
Neural networks
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IF:
6.5
论文数:
2.5W
被引数:
6.5W
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PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
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