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    Classifying data objects
    12.
    发明授权
    Classifying data objects 有权

    公开(公告)号：US11960519B2

    公开(公告)日：2024-04-16

    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

    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.

    Generating vector representations of documents
    19.
    发明授权
    Generating vector representations of documents 有权

    公开(公告)号：US10803380B2

    公开(公告)日：2020-10-13

    申请号：US15262959

    申请日：2016-09-12

    Applicant: Google LLC

    Inventor： Andrew M. Dai ,  Quoc V. Le ,  Gregory Sean Corrado

    IPC: G06N3/08 ,  G06F40/279

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating document vector representations. One of the methods includes obtaining a new document; selecting a plurality of new document word sets; and determining a vector representation for the new document using a trained neural network system, wherein the trained neural network system comprises: a document embedding layer and a classifier, and wherein determining the vector representation for the new document using the trained neural network system comprises iteratively providing each of the plurality of new document word sets to the trained neural network system to determine the vector representation for the new document using gradient descent.

    Predicting likelihoods of conditions being satisfied using recurrent neural networks
    20.
    发明授权
    Predicting likelihoods of conditions being satisfied using recurrent neural networks 有权

    公开(公告)号：US10726327B2

    公开(公告)日：2020-07-28

    申请号：US15588535

    申请日：2017-05-05

    Applicant: Google LLC

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

    IPC: G06N3/08 ,  G06N3/04 ,  G16H50/20 ,  G06N3/063 ,  G06N3/02

    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.

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