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    Providing unlabelled training data for training a computational model
    2.
    发明公开
    Providing unlabelled training data for training a computational model 审中-公开

    公开(公告)号：US20230153611A1

    公开(公告)日：2023-05-18

    申请号：US18049138

    申请日：2022-10-24

    Applicant: Nokia Technologies Oy

    Inventor： Akhil MATHUR ,  Chulhong Min ,  Fahim Kawsar

    IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62

    CPC classification number: G06N3/08 ,  G06K9/6256 ,  G06N3/0472

    Abstract: Providing unlabelled training data for training a computational model comprises:

    obtaining sets of time-aligned unlabelled data, wherein the sets correspond to different ones of a plurality of sensors;
    marking a first sample, of a first set of the sets, as a positive sample, in dependence on statistical separation information indicating a first statistical similarity of at least a portion of the first set to the at least a portion of the reference set and in dependence on the first sample being time-aligned relative to a reference time;
    marking a second sample, of a second set of the sets, as a negative sample, in dependence on statistical separation information indicating a second, lower statistical similarity, of at least a portion of the second set to the at least a portion of the reference set, and in dependence on the second sample being time-misaligned relative to the reference time.

    Updating learned models
    8.
    发明授权
    Updating learned models 有权

    公开(公告)号：US11869662B2

    公开(公告)日：2024-01-09

    申请号：US16954921

    申请日：2018-12-10

    Applicant: Nokia Technologies Oy

    Inventor： Alberto Gil Ramos ,  Sourav Bhattacharya ,  Nicholas Lane ,  Fahim Kawsar

    IPC: G16H50/20 ,  G16H40/67 ,  G16H10/60 ,  G16H50/70 ,  G06N20/00 ,  G06F18/214

    CPC classification number: G16H50/20 ,  G06F18/2148 ,  G06F18/2155 ,  G06N20/00 ,  G16H10/60 ,  G16H40/67 ,  G16H50/70

    Abstract: Methods and systems are disclosed for updating learned models. An embodiment comprises receiving a plurality of data sets representing sensed data from one or more devices and determining, using one or more local learned models, local parameters based on the received data sets. Another operation may comprise generating a combined data set by combining the plurality of data sets and, determining, using one or more local learned models, global parameters based on the combined data set. Another operation may comprise transmitting, to a remote system, the global parameters for determining updated global parameters using one or more global learned models based at least partially on the global parameters, and receiving, from the remote system, the updated global parameters. Another operation may comprise updating the one or more local learned models using both the local parameters and updated global parameters.

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