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    Convolutional gated recurrent neural networks
    2.
    发明授权
    Convolutional gated recurrent neural networks 有权

    公开(公告)号：US10977547B2

    公开(公告)日：2021-04-13

    申请号：US15349867

    申请日：2016-11-11

    Applicant: Google LLC

    Inventor： Lukasz Mieczyslaw Kaiser ,  Ilya Sutskever

    IPC: G06N3/04 ,  G06F17/16 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a convolutional gated recurrent neural network (CGRN). In one of the systems, the CGRN is configured to maintain a state that is a tensor having dimensions x by y by m, wherein x, y, and m are each greater than one, and for each of a plurality of time steps, update a currently maintained state by processing the currently maintained state through a plurality of convolutional gates.

    PREDICTING LIKELIHOODS OF CONDITIONS BEING SATISFIED USING RECURRENT NEURAL NETWORKS
    3.
    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.

    Training neural networks on partitioned training data
    9.
    发明授权
    Training neural networks on partitioned training data 有权

    公开(公告)号：US10380482B2

    公开(公告)日：2019-08-13

    申请号：US14877071

    申请日：2015-10-07

    Applicant: Google LLC

    Inventor： Ilya Sutskever ,  Wojciech Zaremba

    IPC: G06N3/08 ,  G06N3/04 ,  G06N3/10

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes obtaining partitioned training data for the neural network, wherein the partitioned training data comprises a plurality of training items each of which is assigned to a respective one of a plurality of partitions, wherein each partition is associated with a respective difficulty level; and training the neural network on each of the partitions in a sequence from a partition associated with an easiest difficulty level to a partition associated with a hardest difficulty level, wherein, for each of the partitions, training the neural network comprises: training the neural network on a sequence of training items that includes training items selected from the training items in the partition interspersed with training items selected from the training items in all of the partitions.

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