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dc.contributor.authorFrankle, Jonathan
dc.contributor.authorCarbin, Michael James
dc.date.accessioned2021-02-22T18:52:45Z
dc.date.available2021-02-22T18:52:45Z
dc.date.issued2019-05
dc.date.submitted2019-05
dc.identifier.urihttps://hdl.handle.net/1721.1/129953
dc.description.abstractNeural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance. We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. Based on these results, we articulate the lottery ticket hypothesis: dense, randomly-initialized, feed-forward networks contain subnetworks (winning tickets) that-when trained in isolation-reach test accuracy comparable to the original network in a similar number of iterations. The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective. We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations. We consistently find winning tickets that are less than 10-20% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10. Above this size, the winning tickets that we find learn faster than the original network and reach higher test accuracy.en_US
dc.description.sponsorshipOffice of Naval Research (Grant ONR N00014-17-1-2699)en_US
dc.language.isoen
dc.relation.isversionofhttps://openreview.net/forum?id=rJl-b3RcF7en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleThe lottery ticket hypothesis: Finding sparse, trainable neural networksen_US
dc.typeArticleen_US
dc.identifier.citationFrankle, Jonathan and Michael Carbin. "The lottery ticket hypothesis: Finding sparse, trainable neural networks." 7th International Conference on Learning Representations, May 2019, New Orleans, Louisiana, ICLR, May 2019. © 2019 ICLRen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.relation.journal7th International Conference on Learning Representationsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2020-12-04T17:02:04Z
dspace.orderedauthorsFrankle, J; Carbin, Men_US
dspace.date.submission2020-12-04T17:02:07Z
mit.licenseOPEN_ACCESS_POLICY


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