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公开(公告)号:US12282857B1
公开(公告)日:2025-04-22
申请号:US18900506
申请日:2024-09-27
Applicant: Google LLC
Inventor: Siyuan Qiao , Chenxi Liu , Jiahui Yu , Yonghui Wu
IPC: G06N3/088
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training neural networks through contrastive learning. In particular, the contrastive learning is modified to use a relative margin to adjust a training pair's contribution to optimization.
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公开(公告)号:US20210334624A1
公开(公告)日:2021-10-28
申请号:US17365939
申请日:2021-07-01
Applicant: Google LLC
Inventor: Wei Hua , Barret Zoph , Jonathon Shlens , Chenxi Liu , Jonathan Huang , Jia Li , Fei-Fei Li , Kevin Patrick Murphy
Abstract: A method for determining an architecture for a task neural network configured to perform a particular machine learning task is described. The method includes obtaining data specifying a current set of candidate architectures for the task neural network; for each candidate architecture in the current set: processing the data specifying the candidate architecture using a performance prediction neural network having multiple performance prediction parameters, the performance prediction neural network being configured to process the data specifying the candidate architecture in accordance with current values of the performance prediction parameters to generate a performance prediction that characterizes how well a neural network having the candidate architecture would perform after being trained on the particular machine learning task; and generating an updated set of candidate architectures by selecting one or more of the candidate architectures in the current set based on the performance predictions for the candidate architectures in the current set.
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公开(公告)号:US20200257961A1
公开(公告)日:2020-08-13
申请号:US16861491
申请日:2020-04-29
Applicant: Google LLC
Inventor: Wei Hua , Barret Zoph , Jonathon Shlens , Chenxi Liu , Jonathan Huang , Jia Li , Fei-Fei Li , Kevin Patrick Murphy
Abstract: A method for determining an architecture for a task neural network configured to perform a particular machine learning task is described. The method includes obtaining data specifying a current set of candidate architectures for the task neural network; for each candidate architecture in the current set: processing the data specifying the candidate architecture using a performance prediction neural network having multiple performance prediction parameters, the performance prediction neural network being configured to process the data specifying the candidate architecture in accordance with current values of the performance prediction parameters to generate a performance prediction that characterizes how well a neural network having the candidate architecture would perform after being trained on the particular machine learning task; and generating an updated set of candidate architectures by selecting one or more of the candidate architectures in the current set based on the performance predictions for the candidate architectures in the current set.
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公开(公告)号:US11087201B2
公开(公告)日:2021-08-10
申请号:US16861491
申请日:2020-04-29
Applicant: Google LLC
Inventor: Wei Hua , Barret Zoph , Jonathon Shlens , Chenxi Liu , Jonathan Huang , Jia Li , Fei-Fei Li , Kevin Patrick Murphy
Abstract: A method for determining an architecture for a task neural network configured to perform a particular machine learning task is described. The method includes obtaining data specifying a current set of candidate architectures for the task neural network; for each candidate architecture in the current set: processing the data specifying the candidate architecture using a performance prediction neural network having multiple performance prediction parameters, the performance prediction neural network being configured to process the data specifying the candidate architecture in accordance with current values of the performance prediction parameters to generate a performance prediction that characterizes how well a neural network having the candidate architecture would perform after being trained on the particular machine learning task; and generating an updated set of candidate architectures by selecting one or more of the candidate architectures in the current set based on the performance predictions for the candidate architectures in the current set.
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公开(公告)号:US20250111235A1
公开(公告)日:2025-04-03
申请号:US18900506
申请日:2024-09-27
Applicant: Google LLC
Inventor: Siyuan Qiao , Chenxi Liu , Jiahui Yu , Yonghui Wu
IPC: G06N3/088
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training neural networks through contrastive learning. In particular, the contrastive learning is modified to use a relative margin to adjust a training pair's contribution to optimization.
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