Invention Grant
- Patent Title: Quantizing training data sets using ML model metadata
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Application No.: US16924035Application Date: 2020-07-08
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Publication No.: US11645587B2Publication Date: 2023-05-09
- Inventor: Yaniv Ben-Itzhak , Shay Vargaftik
- Applicant: VMware, Inc.
- Applicant Address: US CA Palo Alto
- Assignee: VMware, Inc.
- Current Assignee: VMware, Inc.
- Current Assignee Address: US CA Palo Alto
- Main IPC: G06N20/20
- IPC: G06N20/20 ; G06N5/00 ; G06N5/01

Abstract:
Techniques for quantizing training data sets using machine learning (ML) model metadata are provided. In one set of embodiments, a computer system can receive a training data set comprising a plurality of features and a plurality of data instances, where each data instance includes a feature value for each of the plurality of features. The computer system can further train a machine learning (ML) model using the training data set, where the training results in a trained version of the ML model, and can extract metadata from the trained version of the ML model pertaining to the plurality of features. The computer system can then quantize the plurality of data instances based on the extracted metadata, the quantizing resulting in a quantized version of the training data set.
Public/Granted literature
- US20220012639A1 QUANTIZING TRAINING DATA SETS USING ML MODEL METADATA Public/Granted day:2022-01-13
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