Invention Application
- Patent Title: Optimization of Parameter Values for Machine-Learned Models
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Application No.: US16615303Application Date: 2017-06-02
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Publication No.: US20200167691A1Publication Date: 2020-05-28
- Inventor: Daniel Reuben Golovin , Benjamin Solnik , Subhodeep Moitra , David W. Sculley, II , Gregory Peter Kochanski
- Applicant: Google LLC
- International Application: PCT/US2017/035637 WO 20170602
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06N7/00

Abstract:
A computer-implemented method can include receiving, by one or more computing devices, one or more prior evaluations of performance of a machine learning model, the one or more prior evaluations being respectively associated with one or more prior variants of the machine-learning model, the one or more prior variants of the machine-learning model each having been configured using a different set of adjustable parameter values. The method can include utilizing, by the one or more computing devices, an optimization algorithm to generate a suggested variant of the machine-learning model based at least in part on the one or more prior evaluations of performance and the associated set of adjustable parameter values, the suggested variant of the machine-learning model being defined by a suggested set of adjustable parameter values.
Public/Granted literature
- US12026612B2 Optimization of parameter values for machine-learned models Public/Granted day:2024-07-02
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