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    Generating trained neural networks with increased robustness against adversarial attacks
    1.
    发明授权
    Generating trained neural networks with increased robustness against adversarial attacks 有权

    公开(公告)号：US11481617B2

    公开(公告)日：2022-10-25

    申请号：US16253561

    申请日：2019-01-22

    Applicant: Adobe Inc.

    Inventor： Mayank Singh ,  Nupur Kumari ,  Dhruv Khattar ,  Balaji Krishnamurthy ,  Abhishek Sinha

    IPC: G06N3/08 ,  G06N20/00 ,  H04L9/40

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating trained neural network with increased robustness against adversarial attacks by utilizing a dynamic dropout routine and/or a cyclic learning rate routine. For example, the disclosed systems can determine a dynamic dropout probability distribution associated with neurons of a neural network. The disclosed systems can further drop neurons from a neural network based on the dynamic dropout probability distribution to help neurons learn distinguishable features. In addition, the disclosed systems can utilize a cyclic learning rate routine to force copy weights of a copy neural network away from weights of an original neural network without decreasing prediction accuracy to ensure that the decision boundaries learned are different.

    GENERATING TRAINED NEURAL NETWORKS WITH INCREASED ROBUSTNESS AGAINST ADVERSARIAL ATTACKS
    2.
    发明申请
    GENERATING TRAINED NEURAL NETWORKS WITH INCREASED ROBUSTNESS AGAINST ADVERSARIAL ATTACKS 审中-公开

    公开(公告)号：US20200234110A1

    公开(公告)日：2020-07-23

    申请号：US16253561

    申请日：2019-01-22

    Applicant: Adobe Inc.

    Inventor： Mayank Singh ,  Nupur Kumari ,  Dhruv Khattar ,  Balaji Krishnamurthy ,  Abhishek Sinha

    IPC: G06N3/08 ,  H04L29/06 ,  G06N20/00

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating trained neural network with increased robustness against adversarial attacks by utilizing a dynamic dropout routine and/or a cyclic learning rate routine. For example, the disclosed systems can determine a dynamic dropout probability distribution associated with neurons of a neural network. The disclosed systems can further drop neurons from a neural network based on the dynamic dropout probability distribution to help neurons learn distinguishable features. In addition, the disclosed systems can utilize a cyclic learning rate routine to force copy weights of a copy neural network away from weights of an original neural network without decreasing prediction accuracy to ensure that the decision boundaries learned are different.

    Machine-learning based multi-step engagement strategy modification
    4.
    发明授权
    Machine-learning based multi-step engagement strategy modification 有权

    公开(公告)号：US11107115B2

    公开(公告)日：2021-08-31

    申请号：US16057743

    申请日：2018-08-07

    Applicant: Adobe Inc.

    Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nupur Kumari ,  Nikaash Puri ,  Mayank Singh ,  Eshita Shah ,  Balaji Krishnamurthy ,  Akash Rupela

    IPC: G06Q30/00 ,  G06Q30/02 ,  G06N20/00 ,  G05B19/418

    Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.

    Generating trained neural networks with increased robustness against adversarial attacks
    9.
    发明授权
    Generating trained neural networks with increased robustness against adversarial attacks 有权

    公开(公告)号：US11829880B2

    公开(公告)日：2023-11-28

    申请号：US18049209

    申请日：2022-10-24

    Applicant: Adobe Inc.

    Inventor： Mayank Singh ,  Nupur Kumari ,  Dhruv Khattar ,  Balaji Krishnamurthy ,  Abhishek Sinha

    IPC: G06N3/08 ,  G06N20/00 ,  H04L9/40

    CPC classification number: G06N3/08 ,  G06N20/00 ,  H04L63/1441

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating trained neural network with increased robustness against adversarial attacks by utilizing a dynamic dropout routine and/or a cyclic learning rate routine. For example, the disclosed systems can determine a dynamic dropout probability distribution associated with neurons of a neural network. The disclosed systems can further drop neurons from a neural network based on the dynamic dropout probability distribution to help neurons learn distinguishable features. In addition, the disclosed systems can utilize a cyclic learning rate routine to force copy weights of a copy neural network away from weights of an original neural network without decreasing prediction accuracy to ensure that the decision boundaries learned are different.

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