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    GENERATING A DATA VISUALIZATION GRAPH UTILIZING MODULARITY-BASED MANIFOLD TEARING
    11.
    发明申请
    GENERATING A DATA VISUALIZATION GRAPH UTILIZING MODULARITY-BASED MANIFOLD TEARING 有权

    公开(公告)号：US20220230369A1

    公开(公告)日：2022-07-21

    申请号：US17657255

    申请日：2022-03-30

    Applicant: Adobe Inc.

    Inventor： Nupur Kumari ,  Piyush Gupta ,  Akash Rupela ,  Siddarth R ,  Balaji Krishnamurthy

    IPC: G06T11/20 ,  G06F17/11

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate interactive visual shape representation of digital datasets. For example, the disclosed systems can generate an augmented nearest neighbor network graph from a sampled subset of digital data points using a nearest neighbor model and witness complex model. The disclosed system can further generate a landmark network graph based on the augmented nearest neighbor network graph utilizing a plurality of random walks. The disclosed systems can also generate a loop-augmented spanning network graph based on a partition of the landmark network graph by adding community edges between communities of landmark groups based on modularity and to complete community loops. Based on the loop-augmented spanning network graph, the disclosed systems can generate an interactive visual shape representation for display on a client device.

    Caption association techniques
    14.
    发明授权
    Caption association techniques 有权

    公开(公告)号：US10915701B2

    公开(公告)日：2021-02-09

    申请号：US15925059

    申请日：2018-03-19

    Applicant: Adobe Inc.

    Inventor： Shagun Sodhani ,  Kartikay Garg ,  Balaji Krishnamurthy

    IPC: G06F17/24 ,  G06F40/174 ,  G06N3/08 ,  G06F16/33

    Abstract: Caption association techniques as part of digital content creation by a computing device are described. The computing device is configured to extract text features and bounding boxes from an input document. These text features and bounding boxes are processed to reduce a number of possible search spaces. The processing may involve generating and utilizing a language model that captures the semantic meaning of the text features to identify and filter static text, and may involve identifying and filtering inline captions. A number of bounding boxes are identified for a potential caption. The potential caption and corresponding identified bounding boxes are concatenated into a vector. The concatenated vector is used to identify relationships among the bounding boxes to determine a single bounding box associated with the caption. The determined association is utilized to generate an output digital document that includes a structured association between the caption and a data entry field.

    Facilitating machine-learning and data analysis by computing user-session representation vectors
    16.
    发明授权
    Facilitating machine-learning and data analysis by computing user-session representation vectors 有权

    公开(公告)号：US10726325B2

    公开(公告)日：2020-07-28

    申请号：US15486862

    申请日：2017-04-13

    Applicant: Adobe Inc.

    Inventor： Balaji Krishnamurthy ,  Piyush Gupta ,  Nupur Kumari ,  Akash Rupela

    IPC: G06N3/04 ,  G06N3/08

    Abstract: Disclosed systems and methods generate user-session representation vectors from data generated by user interactions with online services. A transformation application executing on a computing device receives interaction data, which is generated by user devices interacting with an online service. The transformation application separates the interaction data into session datasets. The transformation involves normalizing the session datasets by modifying the rows within each session dataset by removing event identifiers and time stamps. The application transforms each normalized session dataset into a respective user-session representation vector. The application outputs the user-session representation vectors.

    REGULARIZING TARGETS IN MODEL DISTILLATION UTILIZING PAST STATE KNOWLEDGE TO IMPROVE TEACHER-STUDENT MACHINE LEARNING MODELS
    18.
    发明公开
    REGULARIZING TARGETS IN MODEL DISTILLATION UTILIZING PAST STATE KNOWLEDGE TO IMPROVE TEACHER-STUDENT MACHINE LEARNING MODELS 审中-公开

    公开(公告)号：US20240062057A1

    公开(公告)日：2024-02-22

    申请号：US17818506

    申请日：2022-08-09

    Applicant: Adobe Inc.

    Inventor： Surgan Jandial ,  Nikaash Puri ,  Balaji Krishnamurthy

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/0454

    Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that regularize learning targets for a student network by leveraging past state outputs of the student network with outputs of a teacher network to determine a retrospective knowledge distillation loss. For example, the disclosed systems utilize past outputs from a past state of a student network with outputs of a teacher network to compose student-regularized teacher outputs that regularize training targets by making the training targets similar to student outputs while preserving semantics from the teacher training targets. Additionally, the disclosed systems utilize the student-regularized teacher outputs with student outputs of the present states to generate retrospective knowledge distillation losses. Then, in one or more implementations, the disclosed systems compound the retrospective knowledge distillation losses with other losses of the student network outputs determined on the main training tasks to learn parameters of the student networks.

    Generating trained neural networks with increased robustness against adversarial attacks
    20.
    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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