Systems And Methods For Parameter Sharing To Reduce Computational Costs Of Training Machine-Learned Models

    公开(公告)号:US20220108221A1

    公开(公告)日:2022-04-07

    申请号:US17493442

    申请日:2021-10-04

    Applicant: Google LLC

    Abstract: Systems and methods of the present disclosure are directed to a computer-implemented method. The method can include obtaining a machine-learned model comprising a plurality of model units, wherein each model unit comprises a plurality of parameters that are tied to a shared plurality of parameters. The method can include performing a first plurality of training iterations with the machine-learned model to adjust parameters of the shared plurality of parameters. The method can include detecting, based on the first plurality of training iterations, an occurrence of an untying condition. The method can include untying the parameters of one or more model units from the shared plurality of parameters. The method can include performing a second plurality of training iterations with the machine-learned model to adjust parameters of the one or more model units independent of the shared plurality of parameters.

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