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US Patent 9141916 Using embedding functions with a deep network

Patent 9141916 was granted and assigned to Google on September, 2015 by the United States Patent and Trademark Office.

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Is a
Patent
Patent

Patent attributes

Patent Applicant
Google
Google
Current Assignee
Google
Google
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
9141916
Date of Patent
September 22, 2015
Patent Application Number
13803779
Date Filed
March 14, 2013
Patent Citations Received
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US Patent 11954597 Using embedding functions with a deep network
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US Patent 11789699 Systems and methods for private authentication with helper networks
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US Patent 11803752 Separate deployment of machine learning model and associated embedding
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US Patent 11943364 Systems and methods for privacy-enabled biometric processing
0
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US Patent 11677559 Systems and methods for privacy-enabled biometric processing
0
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US Patent 11685048 Enhancing robot learning
0
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US Patent 11748251 Storing tensors in memory based on depth
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US Patent 11756535 System and method for speech recognition using deep recurrent neural networks
...
Patent Primary Examiner
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Michael B. Holmes
Patent abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.

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