Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

659 indexed citations

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This paper, published in 2011, received 659 indexed citations. Written by Benjamin Recht, Christopher Ré, Stephen J. Wright and Feng Niu covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (490 citations), Computer Vision and Pattern Recognition (217 citations) and Computer Networks and Communications (148 citations). Published in Neural Information Processing Systems.

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Fields of papers citing Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent.

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This paper is also available at doi.org/w8300137.

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