MODEL ASSEMBLY WITH KNOWLEDGE DISTILLATION

    公开(公告)号:US20250086493A1

    公开(公告)日:2025-03-13

    申请号:US18243259

    申请日:2023-09-07

    Abstract: In one implementation, a device receives, via a user interface, one or more constraint parameters for each of a plurality of machine learning models that perform different analytics tasks. The device computes, based on the one or more constraint parameters, a set of weights for the plurality of machine learning models. The device generates a unified model by performing knowledge distillation on the plurality of machine learning models using the set of weights. The device deploys the unified model for execution by a particular node in a network.

    PRIVACY PRESERVING PERSON REIDENTIFICATION
    7.
    发明公开

    公开(公告)号:US20240290098A1

    公开(公告)日:2024-08-29

    申请号:US18113175

    申请日:2023-02-23

    CPC classification number: G06V20/48 G06V10/462 G06V10/757

    Abstract: In one embodiment, a device represents each of a plurality of objects depicted in video data captured by a plurality of cameras over time as a set of key points associated with that object. The device forms, for each of the plurality of objects, a set of timeseries of the set of key points associated with that object. The device performs reidentification of a particular one of the plurality of objects across video data captured by two or more of the plurality of cameras by matching sets of timeseries of key points associated with that object derived from video data captured by two or more of the plurality of cameras. The device provides an indication of the reidentification for display to a user.

    AUTOMATED GROUND TRUTH GENERATION USING A NEURO-SYMBOLIC METAMODEL

    公开(公告)号:US20240211747A1

    公开(公告)日:2024-06-27

    申请号:US18087976

    申请日:2022-12-23

    CPC classification number: G06N3/08

    Abstract: In one embodiment, a device receives, from a requestor, a request for a set of ground truth examples of a particular type to be used to train a machine learning model. The request includes context data regarding a location to be analyzed by the machine learning model. The device identifies, based on the request, the set of ground truth examples using a metamodel comprising a semantic reasoner and a sub-symbolic layer. The device associates labels with the set of ground truth examples. The device provides, to the requestor, the set of ground truth examples and their labels.

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