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34 条记录 (耗时 0.024 秒)

    TECHNIQUES FOR MACHINE LANGUAGE MODEL CREATION
    21.
    发明申请
    TECHNIQUES FOR MACHINE LANGUAGE MODEL CREATION 审中-公开

    公开(公告)号：US20200380301A1

    公开(公告)日：2020-12-03

    申请号：US16670914

    申请日：2019-10-31

    申请人： Apple Inc.

    发明人： Michael R. Siracusa ,  Alexander B. Brown ,  Dheeraj Goswami ,  Nathan C. Wertman ,  Jacob T. Sawyer ,  Donald M. Firlik

    IPC分类号： G06K9/62 ,  G06N20/00 ,  G06F8/34 ,  G06F3/0486

    摘要： Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for creating machine learning models. Application developers can select a machine learning template from a plurality of templates appropriate for the type of data used in their application. Templates can include multiple templates for classification of images, text, sound, motion, and tabular data. A graphical user interface allows for intuitive selection of training data, validation data, and integration of the trained model into the application. The techniques further display a numerical score for both the training accuracy and validation accuracy using the test data. The application provides a live mode that allows for execution of the machine learning model on a mobile device to allow for testing the model from data from one or more of the sensors (i.e., camera or microphone) on the mobile device.

    DYNAMIC TASK ALLOCATION FOR NEURAL NETWORKS
    23.
    发明申请
    DYNAMIC TASK ALLOCATION FOR NEURAL NETWORKS 审中-公开

    公开(公告)号：US20180349189A1

    公开(公告)日：2018-12-06

    申请号：US15721716

    申请日：2017-09-29

    申请人： Apple Inc.

    发明人： Francesco Rossi ,  Gaurav Kapoor ,  Michael R. Siracusa ,  William B. March

    IPC分类号： G06F9/50 ,  G06N3/02

    摘要： The subject technology provides for dynamic task allocation for neural network models. The subject technology determines an operation performed at a node of a neural network model. The subject technology assigns an annotation to indicate whether the operation is better performed on a CPU or a GPU based at least in part on hardware capabilities of a target platform. The subject technology determines whether the neural network model includes a second layer. The subject technology, in response to determining that the neural network model includes a second layer, for each node of the second layer of the neural network model, determines a second operation performed at the node. Further the subject technology assigns a second annotation to indicate whether the second operation is better performed on the CPU or the GPU based at least in part on the hardware capabilities of the target platform.

    PACING ACTIVITY DATA OF A USER
    26.
    发明申请
    PACING ACTIVITY DATA OF A USER 审中-公开
    用户的PACING活动数据

    公开(公告)号：US20160058331A1

    公开(公告)日：2016-03-03

    申请号：US14475417

    申请日：2014-09-02

    申请人： APPLE INC.

    发明人： Daniel S. Keen ,  Jay C. Blahnik ,  Gaurav Kapoor ,  Michael R. Siracusa

    IPC分类号： A61B5/11 ,  A61B5/00

    CPC分类号： A61B5/1118 ,  A61B5/4866 ,  A61B5/7275 ,  G06F8/65 ,  G06F9/4843 ,  G06F9/4893 ,  G06F9/542 ,  G06F2209/482 ,  Y02D10/24 ,  Y02D10/42

    摘要： Pacer activity data of a user may be managed. For example, historical activity data of a user corresponding to a particular time of a day prior to a current day may be received. Additionally, a user interface configured to display an activity goal of the user may be generated and the user interface may be provided for presentation. In some aspects, the user interface may be configured to display a first indicator that identifies cumulative progress towards the activity goal and a second indicator that identifies predicted cumulative progress towards the activity goal. The cumulative progress may be calculated based on monitored activity from a start of the current day to the particular time of the current day and the predicted cumulative progress may be calculated based on the received historical activity data corresponding to the particular time of the day prior to the current day.

    摘要翻译： 可以管理用户的步行者活动数据。 例如，可以接收对应于当天之前一天的特定时间的用户的历史活动数据。 此外，可以生成被配置为显示用户的活动目标的用户界面，并且可以提供用户界面用于呈现。 在一些方面，用户界面可以被配置为显示识别针对活动目标的累积进展的第一指示符和识别针对活动目标的预测累积进展的第二指示符。 累积进度可以基于从当天开始到当天的特定时间的监视活动来计算，并且可以基于接收到的与之前一天的特定时间相对应的历史活动数据来计算预测累积进度 当天。

    Integrating machine learning models into an interpreted software development environment
    27.
    发明授权
    Integrating machine learning models into an interpreted software development environment 有权

    公开(公告)号：US11537368B2

    公开(公告)日：2022-12-27

    申请号：US15721722

    申请日：2017-09-29

    申请人： Apple Inc.

    发明人： Alexander B. Brown ,  Michael R. Siracusa ,  Norman N. Wang

    IPC分类号： G06F8/41 ,  G06N20/00 ,  G06N3/10 ,  G06N3/04 ,  G06F8/30

    摘要： The subject technology provides for parsing a line of code in a project of an integrated development environment (IDE). The subject technology executes indirectly, using the interpreter, the parsed line of code. The interpreter references a translated source code document generated by a source code translation component from a machine learning (ML) document written in a particular data format. The translated source code document includes code in a chosen programming language specific to the IDE, and the code of the translated source code document is executable by the interpreter. Further the subject technology provides, by the interpreter, an output of the executed parsed line of code.

    Dynamic operation allocation for neural networks
    30.
    发明授权
    Dynamic operation allocation for neural networks 有权

    公开(公告)号：US10585703B2

    公开(公告)日：2020-03-10

    申请号：US15721716

    申请日：2017-09-29

    申请人： Apple Inc.

    发明人： Francesco Rossi ,  Gaurav Kapoor ,  Michael R. Siracusa ,  William B. March

    IPC分类号： G06F8/41 ,  G06F9/50 ,  G06N3/063 ,  G06N3/02

    摘要： The subject technology provides for dynamic task allocation for neural network models. The subject technology determines an operation performed at a node of a neural network model. The subject technology assigns an annotation to indicate whether the operation is better performed on a CPU or a GPU based at least in part on hardware capabilities of a target platform. The subject technology determines whether the neural network model includes a second layer. The subject technology, in response to determining that the neural network model includes a second layer, for each node of the second layer of the neural network model, determines a second operation performed at the node. Further the subject technology assigns a second annotation to indicate whether the second operation is better performed on the CPU or the GPU based at least in part on the hardware capabilities of the target platform.