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    Vehicle data relation device and methods therefor
    3.
    发明授权
    Vehicle data relation device and methods therefor 有权

    公开(公告)号：US12198049B2

    公开(公告)日：2025-01-14

    申请号：US17211930

    申请日：2021-03-25

    Applicant: Intel Corporation

    Inventor： Ralf Graefe ,  Neslihan Kose Cihangir

    IPC: G06V10/00 ,  G06F18/24 ,  G06N3/08 ,  G06V10/25 ,  G06V20/20 ,  G06V20/58 ,  G06V20/59 ,  G06V20/70 ,  G10L15/08

    Abstract: A vehicle data relation device includes an internal audio/image data analyzer, configured to receive first data representing at least one of audio from within the vehicle or an image from within the vehicle; identify within the first data second data representing an audio indicator or an image indicator, wherein the audio indicator is human speech associated with a significance of an object external to the vehicle, and wherein the image indicator is an action of a human within the vehicle associated with a significance of an object external to the vehicle; an external image analyzer, configured to receive third data representing an image of a vicinity external to the vehicle; identify within the third data an object corresponding to at least one of the audio indicator or the video indicator; and an object data generator, configured to generate data corresponding to the object.

    TRAINING NEURAL NETWORK WITH BUDDING ENSEMBLE ARCHITECTURE BASED ON DIVERSITY LOSS
    4.
    发明公开
    TRAINING NEURAL NETWORK WITH BUDDING ENSEMBLE ARCHITECTURE BASED ON DIVERSITY LOSS 审中-公开

    公开(公告)号：US20230401427A1

    公开(公告)日：2023-12-14

    申请号：US18457002

    申请日：2023-08-28

    Applicant: Intel Corporation

    Inventor： Qutub Syed Sha ,  Neslihan Kose Cihangir ,  Rafael Rosales

    IPC: G06N3/045

    CPC classification number: G06N3/045

    Abstract: Deep neural networks (DNNs) with budding ensemble architectures may be trained using diversity loss. A DNN may include a backbone and a plurality of heads. The backbone includes one or more layers. A layer in the backbone may generate an intermediate tensor. The plurality of heads may include one or more pairs of heads. A pair of heads includes a first head and a second head duplicated from the first head. The second head may include the same tensor operations as the first head but different internal parameters. The intermediate tensor generated by a backbone layer may be input into both the first head and the second head. The first head may compute a first detection tensor, and the second head may compute a second detection tensor. A similarity between the first detection tensor and the second detection tensor may be used as a diversity loss for training the DNN.

    APPARATUS, SYSTEM, AND METHOD OF GENERATING A MULTI-MODEL MACHINE LEARNING (ML) ARCHITECTURE
    8.
    发明申请
    APPARATUS, SYSTEM, AND METHOD OF GENERATING A MULTI-MODEL MACHINE LEARNING (ML) ARCHITECTURE 有权

    公开(公告)号：US20220222927A1

    公开(公告)日：2022-07-14

    申请号：US17710770

    申请日：2022-03-31

    Applicant: Intel Corporation

    Inventor： Rafael Rosales ,  Pablo Munoz ,  Neslihan Kose Cihangir ,  Michael Paulitsch

    IPC: G06V10/776 ,  G06V10/764

    Abstract: For example, an apparatus may include an input to receive Machine Learning (ML) model information corresponding to an ML model to process input information; and a processor to construct a multi-model ML architecture including a plurality of ML model variants based on the ML model, wherein the processor is configured to determine the plurality of ML model variants based on an attribution-based diversity metric corresponding to a model group including a first ML model variant and a second ML model variant, wherein the attribution-based diversity metric corresponding to the model group is based on a diversity between a first attribution scheme and a second attribution scheme, the first attribution scheme representing first portions of the input information attributing to an output of the first ML model variant, the second attribution scheme representing second portions of the input information attributing to an output of the second ML model variant.

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