- 专利标题: Adaptive learning rate schedule in distributed stochastic gradient descent
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申请号: US15938830申请日: 2018-03-28
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公开(公告)号: US11182689B2公开(公告)日: 2021-11-23
- 发明人: Parijat Dube , Sanghamitra Dutta , Gauri Joshi , Priya A. Nagpurkar
- 申请人: INTERNATIONAL BUSINESS MACHINES CORPORATION
- 申请人地址: US NY Armonk
- 专利权人: INTERNATIONAL BUSINESS MACHINES CORPORATION
- 当前专利权人: INTERNATIONAL BUSINESS MACHINES CORPORATION
- 当前专利权人地址: US NY Armonk
- 代理机构: F. Chau & Associates, LLC
- 主分类号: G06N99/00
- IPC分类号: G06N99/00 ; G06N3/08 ; G06F11/34 ; G06N5/04 ; G06N7/08 ; G06K9/62 ; G06F9/50 ; G06N20/00
摘要:
A method for performing machine learning includes assigning processing jobs to a plurality of model learners, using a central parameter server. The processing jobs includes solving gradients based on a current set of parameters. As the results from the processing job are returned, the set of parameters is iterated. A degree of staleness of the solving of the second gradient is determined based on a difference between the set of parameters when the jobs are assigned and the set of parameters when the jobs are returned. The learning rates used to iterate the parameters based on the solved gradients are proportional to the determined degrees of staleness.
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