Invention Grant
- Patent Title: Generating hyper-parameters for machine learning models using modified Bayesian optimization based on accuracy and training efficiency
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Application No.: US16825531Application Date: 2020-03-20
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Publication No.: US11556826B2Publication Date: 2023-01-17
- Inventor: Trung Bui , Lidan Wang , Franck Dernoncourt
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Keller Preece PLLC
- Main IPC: G06N7/00
- IPC: G06N7/00 ; G06N20/20 ; G06N20/10 ; G06K9/62 ; G06N3/08 ; G06V10/75

Abstract:
The present disclosure relates to systems, methods, and non-transitory computer readable media for selecting hyper-parameter sets by utilizing a modified Bayesian optimization approach based on a combination of accuracy and training efficiency metrics of a machine learning model. For example, the disclosed systems can fit accuracy regression and efficiency regression models to observed metrics associated with hyper-parameter sets of a machine learning model. The disclosed systems can also implement a trade-off acquisition function that implements an accuracy-training efficiency balance metric to explore the hyper-parameter feature space and select hyper-parameters for training the machine learning model considering a balance between accuracy and training efficiency.
Public/Granted literature
Information query
IPC分类:
G | 物理 |
G06 | 计算;推算或计数 |
G06N | 基于特定计算模型的计算机系统 |
G06N7/00 | 基于特定数学模式的计算机系统 |