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    Classifying data objects
    1.
    发明授权
    Classifying data objects 有权

    公开(公告)号：US10769191B2

    公开(公告)日：2020-09-08

    申请号：US14576907

    申请日：2014-12-19

    Applicant: Google LLC

    Inventor： Gregory Sean Corrado ,  Tomas Mikolov ,  Samy Bengio ,  Yoram Singer ,  Jonathon Shlens ,  Andrea L. Frome ,  Jeffrey Adgate Dean ,  Mohammad Norouzi

    IPC: G06F16/30 ,  G06F16/35 ,  G06F16/50

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying data objects. One of the methods includes obtaining data that associates each term in a vocabulary of terms with a respective high-dimensional representation of the term; obtaining classification data for a data object, wherein the classification data includes a respective score for each of a plurality of categories, and wherein each of the categories is associated with a respective category label; computing an aggregate high-dimensional representation for the data object from high-dimensional representations for the category labels associated with the categories and the respective scores; identifying a first term in the vocabulary of terms having a high-dimensional representation that is closest to the aggregate high-dimensional representation; and selecting the first term as a category label for the data object.

    Wide and deep machine learning models
    2.
    发明授权
    Wide and deep machine learning models 有权

    公开(公告)号：US10762422B2

    公开(公告)日：2020-09-01

    申请号：US15394668

    申请日：2016-12-29

    Applicant: Google LLC

    Inventor： Tal Shaked ,  Rohan Anil ,  Hrishikesh Balkrishna Aradhye ,  Mustafa Ispir ,  Glen Anderson ,  Wei Chai ,  Mehmet Levent Koc ,  Jeremiah Harmsen ,  Xiaobing Liu ,  Gregory Sean Corrado ,  Tushar Deepak Chandra ,  Heng-Tze Cheng

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.

    CLASSIFYING DATA OBJECTS
    4.
    发明公开
    CLASSIFYING DATA OBJECTS 审中-公开

    公开(公告)号：US20240220527A1

    公开(公告)日：2024-07-04

    申请号：US18606458

    申请日：2024-03-15

    Applicant: Google LLC

    Inventor： Gregory Sean Corrado ,  Tomas Mikolov ,  Samuel Bengio ,  Yoram Singer ,  Jonathon Shlens ,  Andrea L. Frome ,  Jeffrey Adgate Dean ,  Mohammad Norouzi

    IPC: G06F16/35 ,  G06F16/50

    CPC classification number: G06F16/35 ,  G06F16/50

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying data objects. One of the methods includes obtaining data that associates each term in a vocabulary of terms with a respective high-dimensional representation of the term; obtaining classification data for a data object, wherein the classification data includes a respective score for each of a plurality of categories, and wherein each of the categories is associated with a respective category label; computing an aggregate high-dimensional representation for the data object from high-dimensional representations for the category labels associated with the categories and the respective scores; identifying a first term in the vocabulary of terms having a high-dimensional representation that is closest to the aggregate high-dimensional representation; and selecting the first term as a category label for the data object.

    PREDICTING LIKELIHOODS OF CONDITIONS BEING SATISFIED USING RECURRENT NEURAL NETWORKS
    7.
    发明申请
    PREDICTING LIKELIHOODS OF CONDITIONS BEING SATISFIED USING RECURRENT NEURAL NETWORKS 有权

    公开(公告)号：US20210019604A1

    公开(公告)日：2021-01-21

    申请号：US16940131

    申请日：2020-07-27

    Applicant: Google LLC

    Inventor： Gregory Sean Corrado ,  Ilya Sutskever ,  Jeffrey Adgate Dean

    IPC: G06N3/04 ,  G16H50/20 ,  G06N3/063

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting likelihoods of conditions being satisfied using recurrent neural networks. One of the systems is configured to process a temporal sequence comprising a respective input at each of a plurality of time steps and comprises: one or more recurrent neural network layers; one or more logistic regression nodes, wherein each of the logistic regression nodes corresponds to a respective condition from a predetermined set of conditions, and wherein each of the logistic regression nodes is configured to, for each of the plurality of time steps: receive the network internal state for the time step; and process the network internal state for the time step in accordance with current values of a set of parameters of the logistic regression node to generate a future condition score for the corresponding condition for the time step.

    CLASSIFYING DATA OBJECTS
    8.
    发明申请
    CLASSIFYING DATA OBJECTS 审中-公开

    公开(公告)号：US20200380023A1

    公开(公告)日：2020-12-03

    申请号：US16998891

    申请日：2020-08-20

    Applicant: Google LLC

    Inventor： Gregory Sean Corrado ,  Tomas Mikolov ,  Samy Bengio ,  Yoram Singer ,  Jonathon Shlens ,  Andrea L. Frome ,  Jeffrey Adgate Dean ,  Mohammad Norouzi

    IPC: G06F16/35 ,  G06F16/50

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying data objects. One of the methods includes obtaining data that associates each term in a vocabulary of terms with a respective high-dimensional representation of the term; obtaining classification data for a data object, wherein the classification data includes a respective score for each of a plurality of categories, and wherein each of the categories is associated with a respective category label; computing an aggregate high-dimensional representation for the data object from high-dimensional representations for the category labels associated with the categories and the respective scores; identifying a first term in the vocabulary of terms having a high-dimensional representation that is closest to the aggregate high-dimensional representation; and selecting the first term as a category label for the data object.

    Identifying predictive health events in temporal sequences using recurrent neural network
    10.
    发明授权
    Identifying predictive health events in temporal sequences using recurrent neural network 有权

    公开(公告)号：US10402721B2

    公开(公告)日：2019-09-03

    申请号：US15595644

    申请日：2017-05-15

    Applicant: Google LLC

    Inventor： Gregory Sean Corrado ,  Jeffrey Adgate Dean

    IPC: G06N3/02 ,  G06N3/04 ,  G06N3/08 ,  G06N3/10 ,  G16H50/20 ,  G16H50/30

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using recurrent neural networks to analyze health events. One of the methods includes: processing each of a plurality of initial temporal sequences of health events to generate, for each of the initial temporal sequences, a respective network internal state of a recurrent neural network for each time step in the initial temporal sequence; storing, for each of the initial temporal sequences, one or more of the network internal states for the time steps in the temporal sequence in a repository; obtaining a first temporal sequence; processing the first temporal sequence using the recurrent neural network to generate a sequence internal state for the first temporal sequence; and selecting one or more initial temporal sequences that are likely to include health events that are predictive of future health events in the first temporal sequence.

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