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    INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM
    6.
    发明申请
    INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM 有权
    信息处理设备，信息处理方法和程序

    公开(公告)号：US20150046417A1

    公开(公告)日：2015-02-12

    申请号：US14326651

    申请日：2014-07-09

    Applicant: Sony Corporation

    Inventor： Katsuyoshi Kanemoto ,  Kazunori Araki ,  Ryo Nakahashi ,  Masanori Miyahara ,  Kazuki Yoshiyama ,  Takagi Tomohiro

    IPC: G06F17/30

    CPC classification number: G06F17/30997

    Abstract: Disclosed is an information processing apparatus including a metadata expansion unit. The metadata expansion unit is configured to expand metadata of a target item using metadata of other items to which a responder has shown a response, the responder being a user having shown a response to the target item of which the metadata is to be expanded.

    Abstract translation: 公开了一种包括元数据扩展单元的信息处理设备。 元数据扩展单元被配置为使用响应者已经显示了响应的其他项目的元数据来扩展目标项目的元数据，响应者是已经显示对元数据将被扩展的目标项目的响应的用户。

    INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM
    8.
    发明申请
    INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM 有权

    公开(公告)号：US20210350178A1

    公开(公告)日：2021-11-11

    申请号：US17278160

    申请日：2019-09-13

    Applicant: Sony Corporation

    Inventor： Hikaru Ikuta ,  Naoki Ide ,  Kazuki Yoshiyama

    IPC: G06K9/62 ,  G06N3/04

    Abstract: It is aimed to facilitate obtaining of a large number of pieces of data for learning that are necessary to obtain a good-quality learning result.
    A feature value of a first dataset is compared with feature values of a predetermined number of second datasets. A determination as to whether or not each of the predetermined number of second datasets is a dataset usable together with the first dataset is made on the basis of the result of the comparison. For example, the determination is made referring to lacking data information associated with the first dataset. For example, information regarding a second dataset having been determined to be the dataset usable together with the first dataset is presented.

    Information processing method and information processing apparatus
    9.
    发明授权
    Information processing method and information processing apparatus 有权

    公开(公告)号：US10664752B2

    公开(公告)日：2020-05-26

    申请号：US15774850

    申请日：2016-11-25

    Applicant: SONY CORPORATION

    Inventor： Kazuki Yoshiyama ,  Yoshiyuki Kobayashi

    IPC: G06N3/10 ,  G06N3/08 ,  G06F16/838 ,  G06N3/04

    Abstract: There is provided an information processing apparatus and an information processing method to present information for improving development efficiency of a neural network to a user. The information processing method includes: comparing, by a processor, learning results by a plurality of models of neural networks and presenting comparison information relating to the learning results using a graph. In addition, an information processing apparatus includes a control unit configured to acquire learning results by a plurality of models of neural networks and control comparison information relating to the learning results. The control unit presents the comparison information using a graph. In addition, an information processing apparatus includes a comparing unit configured to compare learning results by a plurality of models of neural networks and generate comparison information relating to the learning results. The comparison information is comparison information using a graph.

    Artificial neural network
    10.
    发明授权
    Artificial neural network 有权

    公开(公告)号：US12045715B2

    公开(公告)日：2024-07-23

    申请号：US16244183

    申请日：2019-01-10

    Applicant: Sony Corporation

    Inventor： Javier Alonso Garcia ,  Fabien Cardinaux ,  Kazuki Yoshiyama ,  Thomas Kemp ,  Stephen Tiedemann ,  Stefan Uhlich

    IPC: G06N3/08 ,  G06N3/084 ,  G06N5/04

    CPC classification number: G06N3/08 ,  G06N3/084 ,  G06N5/04

    Abstract: A computer-implemented method of training an artificial neural network (ANN) by generating a first learned parameter for use in normalising input data values during a subsequent inference phase of the trained ANN. The method includes, for each of a series of batches of training data values, deriving a batch variance of the batch of training data values and a running variance of all training data values already processed in the training phase; generating an approximation of a current value of the first learned parameter so that a first scaling factor dependent upon the approximation of the first learned parameter and the running variance, is constrained to be equal to a power of two; and normalizing the batch of input data values by a second scaling factor dependent upon the approximation of the current value of the first learned parameter and the batch variance.

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