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    MACHINE-LEARNING BASED GESTURE RECOGNITION
    1.
    发明申请
    MACHINE-LEARNING BASED GESTURE RECOGNITION 审中-公开

    公开(公告)号：WO2021016484A1

    公开(公告)日：2021-01-28

    申请号：PCT/US2020/043331

    申请日：2020-07-23

    Applicant: APPLE INC.

    Inventor： AVERY, Keith P. ,  DHANANI, Jamil ,  KAUR, Harveen ,  MAUDGALYA, Varun ,  PAEK, Timothy S. ,  RUDCHENKO, Dmytro ,  WESTING, Brandt M. ,  JEONG, Minwoo

    IPC: G06F3/01 ,  G06F3/16 ,  G06N20/00

    Abstract: The subject technology receives, from a first sensor of a device, first sensor output of a first type. The subject technology receives, from a second sensor of the device, second sensor output of a second type, the first and second sensors being non-touch sensors. The subject technology provides the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted touch-based gesture based on sensor output of the first type and sensor output of the second type. The subject technology provides a predicted touch-based gesture based on output from the machine learning model. Further, the subject technology adjusts an audio output level of the device based on the predicted gesture, and where the device is an audio output device.

    COMPILING CODE FOR A MACHINE LEARNING MODEL FOR EXECUTION ON A SPECIALIZED PROCESSOR
    3.
    发明申请
    COMPILING CODE FOR A MACHINE LEARNING MODEL FOR EXECUTION ON A SPECIALIZED PROCESSOR 审中-公开

    公开(公告)号：WO2020242686A1

    公开(公告)日：2020-12-03

    申请号：PCT/US2020/029937

    申请日：2020-04-24

    Applicant: APPLE INC.

    Inventor： PAEK, Timothy S. ,  ROSSI, Francesco ,  DHANANI, Jamil ,  AVERY, Keith P. ,  JEONG, Minwoo ,  SHI, Xiaojin ,  KAUR, Harveen ,  WESTING, Brandt M.

    IPC: G06N3/063 ,  G06N3/04 ,  G06N3/08

    Abstract: The subject technology receives a neural network model in a model format, the model format including information for a set of layers of the neural network model, each layer of the set of layers including a set of respective operations. The subject technology generates neural network (NN) code from the neural network model, the NN code being in a programming language distinct from the model format, and the NN code comprising a respective memory allocation for each respective layer of the set of layers of the neural network model, where the generating comprises determining the respective memory allocation for each respective layer based at least in part on a resource constraint of a target device. The subject technology compiles the NN code into a binary format. The subject technology generates a package for deploying the compiled NN code on the target device.

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