Invention Grant
- Patent Title: Training distilled machine learning models
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Application No.: US17863733Application Date: 2022-07-13
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Publication No.: US11900232B2Publication Date: 2024-02-13
- Inventor: Oriol Vinyals , Jeffrey Adgate Dean , Geoffrey E. Hinton
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: Fish & Richardson P.C.
- Main IPC: G06N20/20
- IPC: G06N20/20 ; G06N7/00 ; G06N20/00 ; G06N3/084 ; G06N3/045 ; G06N7/01

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a distilled machine learning model. One of the methods includes training a cumbersome machine learning model, wherein the cumbersome machine learning model is configured to receive an input and generate a respective score for each of a plurality of classes; and training a distilled machine learning model on a plurality of training inputs, wherein the distilled machine learning model is also configured to receive inputs and generate scores for the plurality of classes, comprising: processing each training input using the cumbersome machine learning model to generate a cumbersome target soft output for the training input; and training the distilled machine learning model to, for each of the training inputs, generate a soft output that matches the cumbersome target soft output for the training input.
Public/Granted literature
- US20220351091A1 TRAINING DISTILLED MACHINE LEARNING MODELS Public/Granted day:2022-11-03
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