Systems and methods for out-of-distribution classification

    公开(公告)号:US11481636B2

    公开(公告)日:2022-10-25

    申请号:US16877325

    申请日:2020-05-18

    Abstract: An embodiment provided herein preprocesses the input samples to the classification neural network, e.g., by adding Gaussian noise to word/sentence representations to make the function of the neural network satisfy Lipschitz property such that a small change in the input does not cause much change to the output if the input sample is in-distribution. Method to induce properties in the feature representation of neural network such that for out-of-distribution examples the feature representation magnitude is either close to zero or the feature representation is orthogonal to all class representations. Method to generate examples that are structurally similar to in-domain and semantically out-of domain for use in out-of-domain classification training. Method to prune feature representation dimension to mitigate long tail error of unused dimension in out-of-domain classification. Using these techniques, the accuracy of both in-domain and out-of-distribution identification can be improved.

    Systems and Methods for Out-of-Distribution Classification

    公开(公告)号:US20210150365A1

    公开(公告)日:2021-05-20

    申请号:US16877325

    申请日:2020-05-18

    Abstract: An embodiment provided herein preprocesses the input samples to the classification neural network, e.g., by adding Gaussian noise to word/sentence representations to make the function of the neural network satisfy Lipschitz property such that a small change in the input does not cause much change to the output if the input sample is in-distribution. Method to induce properties in the feature representation of neural network such that for out-of-distribution examples the feature representation magnitude is either close to zero or the feature representation is orthogonal to all class representations. Method to generate examples that are structurally similar to in-domain and semantically out-of domain for use in out-of-domain classification training. Method to prune feature representation dimension to mitigate long tail error of unused dimension in out-of-domain classification. Using these techniques, the accuracy of both in-domain and out-of-distribution identification can be improved.

    Connected contact identification
    6.
    发明授权

    公开(公告)号:US10897520B2

    公开(公告)日:2021-01-19

    申请号:US16262488

    申请日:2019-01-30

    Abstract: A database server may analyze interaction data including communication to generate a graph representation of various users and connections between the users. The database server may utilize the graph representation of connections to identify sufficiently connected target user identifiers in one or more external organizations. A connection metric may be assigned to each user identifier of one or more groups of user identifiers generated using the graph representation, and the target user identifiers may be identified based on the connection metrics.

    CONNECTED CONTACT IDENTIFICATION
    7.
    发明申请

    公开(公告)号:US20200153934A1

    公开(公告)日:2020-05-14

    申请号:US16262488

    申请日:2019-01-30

    Abstract: A database server may analyze interaction data including communication to generate a graph representation of various users and connections between the users. The database server may utilize the graph representation of connections to identify sufficiently connected target user identifiers in one or more external organizations. A connection metric may be assigned to each user identifier of one or more groups of user identifiers generated using the graph representation, and the target user identifiers may be identified based on the connection metrics.

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