中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

11 records (took 0.019 seconds)

    Method for analyzing a prediction classification in a machine learning model
    1.
    发明授权
    Method for analyzing a prediction classification in a machine learning model 有权

    公开(公告)号：US11410057B2

    公开(公告)日：2022-08-09

    申请号：US16795774

    申请日：2020-02-20

    Applicant: NXP B.V.

    Inventor： Brian Ermans ,  Wilhelmus Petrus Adrianus Johannus Michiels ,  Christine van Vredendaal

    IPC: G06N5/04 ,  G06F16/901 ,  G06N20/00

    Abstract: A method is provided for analyzing a classification in a machine learning model (ML). In the method, the ML model is trained using a training dataset to produce a trained ML model. One or more samples are provided to the trained ML model to produce one or more prediction classifications. A gradient is determined for the one of more samples at a predetermined layer of the trained ML model. The one or more gradients and the one or more prediction classifications for each sample are stored. Also, an intermediate value of the ML model may be stored. Then, a sample is chosen to analyze. A gradient of the sample is determined if the gradient was not already determined when the at least one gradient is determined. Using the at least one gradient, and one or more of a data structure, a predetermined metric, and an intermediate value, the k nearest neighbors to the sample are determined. A report comprising the sample and the k nearest neighbors may be provided for analysis.

    DATA SAMPLE ANALYSIS IN A DATASET FOR A MACHINE LEARNING MODEL
    2.
    发明申请
    DATA SAMPLE ANALYSIS IN A DATASET FOR A MACHINE LEARNING MODEL 有权

    公开(公告)号：US20210406693A1

    公开(公告)日：2021-12-30

    申请号：US16912052

    申请日：2020-06-25

    Applicant: NXP B.V.

    Inventor： Christine VAN VREDENDAAL ,  Wilhelmus Petrus Adrianus Johannus Michiels ,  Gerardus Antonius Franciscus Derks ,  Brian Ermans

    IPC: G06N3/08 ,  G06N20/00 ,  G06K9/62

    Abstract: A method is described for analyzing data samples of a machine learning (ML) model to determine why the ML model classified a sample like it did. Two samples are chosen for analysis. The two samples may be nearest neighbors. Samples classified as nearest neighbors are typically samples that are more similar with respect to a predetermined criterion than other samples of a set of samples. In the method, a first set of features of a first sample and a second set of features of a second sample are collected. A set of overlapping features of the first and second sets of features is determined. Then, the set of overlapping features is analyzed using a predetermined visualization technique to determine why the ML model determined the first sample to be similar to the second sample.

    Method and machine learning system for detecting adversarial examples
    3.
    发明授权
    Method and machine learning system for detecting adversarial examples 有权

    公开(公告)号：US11501206B2

    公开(公告)日：2022-11-15

    申请号：US16576830

    申请日：2019-09-20

    Applicant: NXP B.V.

    Inventor： Brian Ermans ,  Peter Doliwa ,  Christine van Vredendaal

    IPC: G06N20/00 ,  G06K9/62 ,  G06N3/08

    Abstract: A method and machine learning system for detecting adversarial examples is provided. A first machine learning model is trained with a first machine learning training data set having only training data samples with robust features. A second machine learning model is trained with a second machine learning training data set, the second machine learning training data set having only training data samples with non-robust features. A feature is a distinguishing element in a data sample. A robust feature is more resistant to adversarial perturbations than a non-robust feature. A data sample is provided to each of the first and second trained machine learning models during an inference operation. if the first trained machine learning model classifies the data sample with high confidence, and the second trained machine learning model classifies the data sample differently with a high confidence, then the data sample is determined to be an adversarial example.

    Method and system for protecting a machine learning model against extraction
    4.
    发明授权
    Method and system for protecting a machine learning model against extraction 有权

    公开(公告)号：US11321456B2

    公开(公告)日：2022-05-03

    申请号：US16414068

    申请日：2019-05-16

    Applicant: NXP B.V.

    Inventor： Gerardus Antonius Franciscus Derks ,  Brian Ermans ,  Wilhelmus Petrus Adrianus Johannus Michiels ,  Christine van Vredendaal

    IPC: G06F21/52 ,  G06N3/08 ,  G06F21/55

    Abstract: A method for protecting a machine learning (ML) model is provided. During inference operation of the ML model, a plurality of input samples is provided to the ML model. A distribution of a plurality of output predictions from a predetermined node in the ML model is measured. If the distribution of the plurality of output predictions indicates correct output category prediction with low confidence, then the machine learning model is slowed to reduce a prediction rate of subsequent output predictions. If the distribution of the plurality of categories indicates correct output category prediction with a high confidence, then the machine learning model is not slowed to reduce the prediction rate of subsequent output predictions of the machine learning model. A moving average of the distribution may be used to determine the speed reduction. This makes a cloning attack on the ML model take longer with minimal impact to a legitimate user.

    METHOD FOR GENERATING A DETAILED VISUALIZATION OF MACHINE LEARNING MODEL BEHAVIOR
    6.
    发明申请
    METHOD FOR GENERATING A DETAILED VISUALIZATION OF MACHINE LEARNING MODEL BEHAVIOR 有权

    公开(公告)号：US20230040470A1

    公开(公告)日：2023-02-09

    申请号：US17444682

    申请日：2021-08-09

    Applicant: NXP B.V.

    Inventor： Brian Ermans ,  Peter Doliwa ,  Gerardus Antonius Franciscus Derks ,  Wilhelmus Petrus Adrianus Johannus Michiels ,  Frederik Dirk Schalij

    IPC: G06K9/62 ,  G06N3/08

    Abstract: A method is provided for generating a visualization for explaining a behavior of a machine learning (ML) model. In the method, an image is input to the ML model for an inference operation. The input image has an increased resolution compared to an image resolution the ML model was intended to receive as an input. A resolution of a plurality of resolution-independent convolutional layers of the neural network are adjusted because of the increased resolution of the input image. A resolution-independent convolutional layer of the neural network is selected. The selected resolution-independent convolutional layer is used to generate a plurality of activation maps. The plurality of activation maps is used in a visualization method to show what features of the image were important for the ML model to derive an inference conclusion. The method may be implemented in a computer program having instructions executable by a processor.

    METHOD FOR ANALYZING AN OUTPUT OF AN OBJECT DETECTOR
    7.
    发明申请
    METHOD FOR ANALYZING AN OUTPUT OF AN OBJECT DETECTOR 有权

    公开(公告)号：US20220261571A1

    公开(公告)日：2022-08-18

    申请号：US17176583

    申请日：2021-02-16

    Applicant: NXP B.V.

    Inventor： Gerardus Antonius Franciscus DERKS ,  Wilhelmus Petrus Adrianus Johannus Michiels ,  Brian Ermans ,  Frederik Dirk Schalij

    IPC: G06K9/00 ,  G06K9/32 ,  G06K9/62 ,  G06N5/04 ,  G06N20/00

    Abstract: A method is described for analyzing an output of an object detector for a selected object of interest in an image. The object of interest in a first image is selected. A user of the object detector draws a bounding box around the object of interest. A first inference operation is run on the first image using the object detector, and in response, the object detect provides a plurality of proposals. A non-max suppression (NMS) algorithm is run on the plurality of proposals, including the proposal having the object of interest. A classifier and bounding box regressor are run on each proposal of the plurality of proposals and results are outputted. The outputted results are then analyzed. The method can provide insight into why an object detector returns the results that it does.

    METHOD FOR ANALYZING CLASS SIMILARITIES IN A MACHINE LEARNING MODEL
    9.
    发明申请
    METHOD FOR ANALYZING CLASS SIMILARITIES IN A MACHINE LEARNING MODEL 有权

    公开(公告)号：US20220067503A1

    公开(公告)日：2022-03-03

    申请号：US17002978

    申请日：2020-08-26

    Applicant: NXP B.V.

    Inventor： Brian Ermans ,  Gerardus Antonius Franciscus Derks ,  Wilhelmus Petrus Adrianus Johannus Michiels ,  Christine van Vredendaal

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A method is provided for analyzing a similarly between classes of a plurality of classes in a trained machine learning model (ML). The method includes collecting weights of connections from each node of a first predetermined layer of a neural network (NN) to each node of a second predetermined layer of the NN to which the nodes of the first predetermined layer are connected. The collected weights are used to calculate distances from each node of the first predetermined layer to nodes of the second predetermined layer to which the first predetermined layer nodes are connected. The distances are compared to determine which classes the NN determines are similar. Two or more of the similar classes may then be analyzed using any of a variety of techniques to determine why the two or more classes of the NN were determined to be similar.

    METHOD AND MACHINE LEARNING SYSTEM FOR DETECTING ADVERSARIAL EXAMPLES
    10.
    发明申请
    METHOD AND MACHINE LEARNING SYSTEM FOR DETECTING ADVERSARIAL EXAMPLES 有权

    公开(公告)号：US20210089957A1

    公开(公告)日：2021-03-25

    申请号：US16576830

    申请日：2019-09-20

    Applicant: NXP B.V.

    Inventor： Brian Ermans ,  Peter Doliwa ,  Christine van Vredendaal

    IPC: G06N20/00 ,  G06K9/62

    Abstract: A method and machine learning system for detecting adversarial examples is provided. A first machine learning model is trained with a first machine learning training data set having only training data samples with robust features. A second machine learning model is trained with a second machine learning training data set, the second machine learning training data set having only training data samples with non-robust features. A feature is a distinguishing element in a data sample. A robust feature is more resistant to adversarial perturbations than a non-robust feature. A data sample is provided to each of the first and second trained machine learning models during an inference operation. if the first trained machine learning model classifies the data sample with high confidence, and the second trained machine learning model classifies the data sample differently with a high confidence, then the data sample is determined to be an adversarial example.

Patent Agency Ranking