公开(公告)号：US20190303787A1

公开(公告)日：2019-10-03

申请号：US15938830

申请日：2018-03-28

申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION

发明人： PARIJAT DUBE ,  Sanghamitra Dutta ,  Gauri Joshi ,  Priya A. Nagpurkar

IPC分类号： G06N7/08 ,  G06F9/50 ,  G06K9/62 ,  G06F15/18

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.

公开(公告)号：US11182689B2

公开(公告)日：2021-11-23

申请号：US15938830

申请日：2018-03-28

申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION

发明人： Parijat Dube ,  Sanghamitra Dutta ,  Gauri Joshi ,  Priya A. Nagpurkar

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.