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    Classifying data objects
    32.
    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.

    DEVICE PLACEMENT OPTIMIZATION WITH REINFORCEMENT LEARNING
    37.
    发明申请
    DEVICE PLACEMENT OPTIMIZATION WITH REINFORCEMENT LEARNING 审中-公开

    公开(公告)号：US20200279163A1

    公开(公告)日：2020-09-03

    申请号：US16878720

    申请日：2020-05-20

    Applicant: Google LLC

    Inventor： Samuel Bengio ,  Mohammad Norouzi ,  Benoit Steiner ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le ,  Naveen Kumar ,  Yuefeng Zhou ,  Rasmus Munk Larsen

    IPC: G06N3/08 ,  G06N5/04 ,  G06N3/04 ,  G06N3/10

    Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.

    HIERARCHICAL DEVICE PLACEMENT WITH REINFORCEMENT LEARNING
    38.
    发明申请
    HIERARCHICAL DEVICE PLACEMENT WITH REINFORCEMENT LEARNING 审中-公开

    公开(公告)号：US20190392294A1

    公开(公告)日：2019-12-26

    申请号：US16554217

    申请日：2019-08-28

    Applicant: Google LLC

    Inventor： Benoit Steiner ,  Anna Darling Goldie ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le

    IPC: G06N3/04 ,  G06N20/00 ,  G06F16/901 ,  G06N3/063 ,  G06N3/08 ,  G06N5/04

    Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices includes receiving data specifying machine learning operations, and determining a placement that assigns each of the operations specified by the data to a respective device from the multiple hardware devices. Determining the placement includes: generating, from the data, a respective operation embedding for each of the operations; grouping the operations into multiple operation groups, comprising processing each of the respective operation embeddings using a grouper neural network having multiple grouper parameters, in which the grouper neural network is configured to, for each of the operations, process the operation embedding for the operation in accordance with first values of the grouper parameters to generate a grouper output that assigns the operation to an operation group from the multiple operation groups; and assigning each of the operation groups to a respective device from the multiple hardware devices.

    Suggesting and/or providing ad serving constraint information
    39.
    发明授权
    Suggesting and/or providing ad serving constraint information 有权

    公开(公告)号：US10482503B2

    公开(公告)日：2019-11-19

    申请号：US16055004

    申请日：2018-08-03

    Applicant: GOOGLE LLC

    Inventor： Jeffrey Adgate Dean ,  Georges Harik ,  Paul Buchheit

    IPC: G06F16/00 ,  G06Q30/02 ,  G06F16/2455

    Abstract: Targeting information (also referred to as ad “serving constraints”) or candidate targeting information for an advertisement is identified. Targeting information may be identified by extracting topics or concepts from, and/or generating topics or concepts based on, ad information, such as information from a Web page to which an ad is linked (or some other Web page of interest to the ad or advertiser). The topics or concepts may be relevant queries associated with the Web page of interest, clusters, etc.

    DEVICE PLACEMENT OPTIMIZATION WITH REINFORCEMENT LEARNING
    40.
    发明申请
    DEVICE PLACEMENT OPTIMIZATION WITH REINFORCEMENT LEARNING 审中-公开

    公开(公告)号：US20190303761A1

    公开(公告)日：2019-10-03

    申请号：US16445330

    申请日：2019-06-19

    Applicant: Google LLC

    Inventor： Samy Bengio ,  Mohammad Edward Norouzi ,  Benoit Steiner ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le ,  Naveen Kumar ,  Yuefeng Zhou ,  Rasmus Munk Larsen

    IPC: G06N3/08 ,  G06N5/04

    Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.

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