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US Patent 10528866 Training a document classification neural network

Patent 10528866 was granted and assigned to Google on January, 2020 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
0

Patent attributes

Patent Applicant
Google
Google
0
Current Assignee
Google
Google
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
105288660
Patent Inventor Names
Andrew M. Dai0
Quoc V. Le0
Date of Patent
January 7, 2020
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Patent Application Number
152575390
Date Filed
September 6, 2016
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Patent Citations Received
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US Patent 12067363 System, method, and computer program for text sanitization
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US Patent 11521639 Speech sentiment analysis using a speech sentiment classifier pretrained with pseudo sentiment labels
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US Patent 11783164 Joint many-task neural network model for multiple natural language processing (NLP) tasks
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US Patent 11397887 Dynamic tuning of training parameters for machine learning algorithms
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US Patent 11853709 Text translation method and apparatus, storage medium, and computer device
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US Patent 11868888 Training a document classification neural network
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US Patent 11462037 System and method for automated analysis of electronic travel data
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US Patent 10963501 Systems and methods for generating a topic tree for digital information
0
...
Patent Primary Examiner
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Hal Schnee
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Patent abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a document classification neural network. One of the methods includes training an autoencoder neural network to autoencode input documents, wherein the autoencoder neural network comprises the one or more LSTM neural network layers and an autoencoder output layer, and wherein training the autoencoder neural network comprises determining pre-trained values of the parameters of the one or more LSTM neural network layers from initial values of the parameters of the one or more LSTM neural network layers; and training the document classification neural network on a plurality of training documents to determine trained values of the parameters of the one or more LSTM neural network layers from the pre-trained values of the parameters of the one or more LSTM neural network layers.

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