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公开(公告)号：US20200005045A1
公开(公告)日：2020-01-02
申请号：US16024814
申请日：2018-06-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Christopher Wright Lloyd, II , Konstantin Salomatin , Jeffrey Douglas Gee , Mahesh S. Joshi , Shivani Rao , Vladislav Tcheprasov , Gungor Polatkan , Deepak Kumar Dileep Kumar
Abstract: Techniques for implementing a feature generation pipeline for machine learning are provided. In one technique, multiple jobs are executed, each of which computes a different set of feature values for a different feature of multiple features associated with videos. A feature registry is stored that lists each of the multiple features. After the jobs are executed and the feature registry is stored, a model specification is received that indicates a set of features for a model. For each feature in a subset of the set of features, a location is identified in storage where a value for said each feature is found and the value for that feature is retrieved from the location. A feature vector is created that comprises, for each feature in the set of features, the value that corresponds to that feature. The feature vector is used to train the model or as input to the model.
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公开(公告)号：US11195023B2
公开(公告)日：2021-12-07
申请号：US16024814
申请日：2018-06-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Christopher Wright Lloyd, II , Konstantin Salomatin , Jeffrey Douglas Gee , Mahesh S. Joshi , Shivani Rao , Vladislav Tcheprasov , Gungor Polatkan , Deepak Kumar Dileep Kumar
Abstract: Techniques for implementing a feature generation pipeline for machine learning are provided. In one technique, multiple jobs are executed, each of which computes a different set of feature values for a different feature of multiple features associated with videos. A feature registry is stored that lists each of the multiple features. After the jobs are executed and the feature registry is stored, a model specification is received that indicates a set of features for a model. For each feature in a subset of the set of features, a location is identified in storage where a value for said each feature is found and the value for that feature is retrieved from the location. A feature vector is created that comprises, for each feature in the set of features, the value that corresponds to that feature. The feature vector is used to train the model or as input to the model.
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公开(公告)号：US10733243B2
公开(公告)日：2020-08-04
申请号：US15691623
申请日：2017-08-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Runfang Zhou , Ajit Paul Singh , Anish Ramdas Nair , Sen Zhou , Vladislav Tcheprasov , Sachin Hosmani , Da Teng
IPC: G06F16/00 , G06F16/951 , G06Q50/00
Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method described herein are directed to a Similar Profiles Engine. The Similar Profiles Engine generates an inverted index query based on one or more portions of profile data of a target member account of a social network service. The Similar Profiles Engine identifies respective profile data, of one or more candidate member accounts in the social network service, that maps to at least one inverted index filter, the at least one inverted index filter matching at least a portion of the inverted index query. The Similar Profiles Engine calculates a similarity score between each respective candidate member account and the target member account, and causes a display of identifiers of one or more candidate member accounts in a user interface of a client device based on respective similarity scores.
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