中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

6 records (took 0.01 seconds)

    MODEL-AGNOSTIC MULTI-FACTOR METRIC DRIFT ATTRIBUTION
    4.
    发明申请
    MODEL-AGNOSTIC MULTI-FACTOR METRIC DRIFT ATTRIBUTION 有权

    公开(公告)号：US20240420009A1

    公开(公告)日：2024-12-19

    申请号：US18210756

    申请日：2023-06-16

    Applicant: Adobe Inc.

    Inventor： Nimish Srivastav ,  Vijay Srivastava ,  Deepak Pai

    IPC: G06N20/00

    Abstract: Multi-factor metric drift evaluation and attribution techniques are described. A drift attribution model is trained to compute, for a segment of input data that defines an observed value for a metric and observed values for each of a plurality of factors that influence the value of the metric, a contribution by each of the plurality of factors to the observed metric value. Drift observations output by the trained drift attribution model are further processed using a Shapely explainer to represent contributions of each of the metric factors, and their associated values, relative to one or more observed values of a metric during the time segment. The respective magnitude by which each metric factor affects an observed value of the metric is described in a metric drift report, which objectively quantifies respective impacts of a factor, relative to other factors that affect a metric.

    REDUCING BIAS IN MACHINE LEARNING MODELS UTILIZING A FAIRNESS DEVIATION CONSTRAINT AND DECISION MATRIX
    5.
    发明公开
    REDUCING BIAS IN MACHINE LEARNING MODELS UTILIZING A FAIRNESS DEVIATION CONSTRAINT AND DECISION MATRIX 审中-公开

    公开(公告)号：US20230393960A1

    公开(公告)日：2023-12-07

    申请号：US17805377

    申请日：2022-06-03

    Applicant: Adobe Inc.

    Inventor： Meghanath Macha Yadagiri ,  Anish Narang ,  Deepak Pai ,  Sriram Ravindran ,  Vijay Srivastava

    IPC: G06F11/34 ,  G06K9/62 ,  G06N20/00

    CPC classification number: G06F11/3452 ,  G06K9/6267 ,  G06K9/6263 ,  G06N20/00

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that control bias in machine learning models by utilizing a fairness deviation constraint to learn a decision matrix that modifies machine learning model predictions. In one or more embodiments, the disclosed systems generate, utilizing a machine learning model, predicted classification probabilities from a plurality of samples comprising a plurality of values for a data attribute. Moreover, the disclosed systems determine utilizing a decision matrix and the predicted classification probabilities, that the machine learning model fails to satisfy a fairness deviation constraint with respect to a value of the data attribute. In addition, the disclosed systems generate a modified decision matrix for the machine learning model to satisfy the fairness deviation constraint by selecting a modified decision threshold for the value of the data attribute.

    Anomaly detection and reporting for machine learning models
    6.
    发明授权
    Anomaly detection and reporting for machine learning models 有权

    公开(公告)号：US11449712B2

    公开(公告)日：2022-09-20

    申请号：US16220333

    申请日：2018-12-14

    Applicant: ADOBE INC.

    Inventor： Deepak Pai ,  Vijay Srivastava ,  Joshua Sweetkind-Singer ,  Shankar Venkitachalam

    IPC: G06K9/62 ,  G06F16/904 ,  G06N20/00 ,  G06N5/00

    Abstract: In various embodiments of the present disclosure, output data generated by a deployed machine learning model may be received. An input data anomaly may be detected based at least in part on analyzing input data of the deployed machine learning model. An output data anomaly may further be detected based at least in part on analyzing the output data of the deployed machine learning model. A determination may be made that the input data anomaly contributed to the output data anomaly based at least in part on comparing the input data anomaly to the output data anomaly. A report may be generated that is indicative of the input data anomaly and the output data anomaly, and the report may be transmitted to a client device.

Patent Agency Ranking