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    MACHINE LEARNING MODEL INTERPRETATION
    61.
    发明申请
    MACHINE LEARNING MODEL INTERPRETATION 审中-公开

    公开(公告)号：US20190156216A1

    公开(公告)日：2019-05-23

    申请号：US15815899

    申请日：2017-11-17

    Applicant: Adobe Inc.

    Inventor： Piyush Gupta ,  Nikaash Puri ,  Balaji Krishnamurthy

    IPC: G06N3/08 ,  G06N5/02 ,  G06F15/18 ,  G06F17/30

    Abstract: A technique is disclosed for generating class level rules that globally explain the behavior of a machine learning model, such as a model that has been used to solve a classification problem. Each class level rule represents a logical conditional statement that, when the statement holds true for one or more instances of a particular class, predicts that the respective instances are members of the particular class. Collectively, these rules represent the pattern followed by the machine learning model. The techniques are model agnostic, and explain model behavior in a relatively easy to understand manner by outputting a set of logical rules that can be readily parsed. Although the techniques can be applied to any number of applications, in some embodiments, the techniques are suitable for interpreting models that perform the task of classification. Other machine learning model applications can equally benefit.

    TRAJECTORY-BASED EXPLAINABILITY FRAMEWORK FOR REINFORCEMENT LEARNING MODELS
    62.
    发明申请
    TRAJECTORY-BASED EXPLAINABILITY FRAMEWORK FOR REINFORCEMENT LEARNING MODELS 有权

    公开(公告)号：US20240403651A1

    公开(公告)日：2024-12-05

    申请号：US18328174

    申请日：2023-06-02

    Applicant: Adobe Inc.

    Inventor： Shripad Vilasrao Deshmukh ,  Arpan Dasgupta ,  Balaji Krishnamurthy ,  Chirag Agarwal ,  Georgios Theocharous ,  Jayakumar Subramanian

    IPC: G06N3/092

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that provide a trajectory-based explainability framework for reinforcement learning models. For example, the disclosed systems generate trajectory clusters from trajectories utilized to train a reinforcement learning agent. In some embodiments, the disclosed system generates a complementary target data set by removing a target trajectory cluster from the trajectory clusters. In some cases, the disclosed system trains a test reinforcement learning agent utilizing the complementary target data set and generates a cluster attribution by comparing the result of the test reinforcement learning agent with the result of the reinforcement learning agent.

    Form structure similarity detection
    63.
    发明授权
    Form structure similarity detection 有权

    公开(公告)号：US12124497B1

    公开(公告)日：2024-10-22

    申请号：US18190686

    申请日：2023-03-27

    Applicant: Adobe Inc.

    Inventor： Abhinav Java ,  Surgan Jandial ,  Shripad Vilasrao Deshmukh ,  Milan Aggarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy ,  Arneh Jain

    IPC: G06F16/383 ,  G06F16/332 ,  G06V30/19 ,  G06V30/412

    CPC classification number: G06F16/383 ,  G06F16/332 ,  G06V30/19147 ,  G06V30/412

    Abstract: Form structure similarity detection techniques are described. A content processing system, for instance, receives a query snippet that depicts a query form structure. The content processing system generates a query layout string that includes semantic indicators to represent the query form structure and generates candidate layout strings that represent form structures from a target document. The content processing system calculates similarity scores between the query layout string and the candidate layout strings. Based on the similarity scores, the content processing system generates a target snippet for display that depicts a form structure that is structurally similar to the query form structure. The content processing system is further operable to generate a training dataset that includes image pairs of snippets depicting form structures that are structurally similar. The content processing system utilizes the training dataset to train a machine learning model to perform form structure similarity matching.

    Form structure extraction by predicting associations
    65.
    发明授权
    Form structure extraction by predicting associations 有权

    公开(公告)号：US12086728B2

    公开(公告)日：2024-09-10

    申请号：US18135948

    申请日：2023-04-18

    Applicant: Adobe Inc.

    Inventor： Milan Aggarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy

    IPC: G06N5/04 ,  G06N3/08 ,  G06N20/00 ,  G06N20/10 ,  G06V10/82

    CPC classification number: G06N5/04 ,  G06N3/08 ,  G06N20/00 ,  G06N20/10 ,  G06V10/82

    Abstract: Techniques described herein extract form structures from a static form to facilitate making that static form reflowable. A method described herein includes accessing low-level form elements extracted from a static form. The method includes determining, using a first set of prediction models, second-level form elements based on the low-level form elements. Each second-level form element includes a respective one or more low-level form elements. The method further includes determining, using a second set of prediction models, high-level form elements based on the second-level form elements and the low-level form elements. Each high-level form element includes a respective one or more second-level form elements or low-level form elements. The method further includes generating a reflowable form based on the static form by, for each high-level form element, linking together the respective one or more second-level form elements or low-level form elements.

    NON-ADVERSARIAL IMAGE GENERATION USING TRANSFER LEARNING
    67.
    发明公开
    NON-ADVERSARIAL IMAGE GENERATION USING TRANSFER LEARNING 审中-公开

    公开(公告)号：US20240242394A1

    公开(公告)日：2024-07-18

    申请号：US18097856

    申请日：2023-01-17

    Applicant: Adobe Inc.

    Inventor： Puneet Mangla ,  Balaji Krishnamurthy

    IPC: G06T11/00 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

    CPC classification number: G06T11/00 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

    Abstract: In implementations of systems for non-adversarial image generation using transfer learning, a computing device implements a generation system to receive input data describing random noise. The generation system generates a latent representation in a latent space of a machine learning model based on the random noise using a transformer model that is trained to generate latent representations in the latent space. A digital image is generated using the machine learning model based on the latent representation that depicts an object that is visually similar to objects depicted in digital images of a training dataset used to train the machine learning model based on a perceptual loss.

    Language model with external knowledge base
    68.
    发明授权
    Language model with external knowledge base 有权

    公开(公告)号：US11997056B2

    公开(公告)日：2024-05-28

    申请号：US17897419

    申请日：2022-08-29

    Applicant: ADOBE INC.

    Inventor： Sumit Bhatia ,  Jivat Neet Kaur ,  Rachit Bansal ,  Milan Aggarwal ,  Balaji Krishnamurthy

    IPC: H04L51/02 ,  G06F40/295 ,  G06N5/022

    CPC classification number: H04L51/02 ,  G06F40/295 ,  G06N5/022

    Abstract: The technology described herein receives a natural-language sequence of words comprising multiple entities. The technology then identifies a plurality of entities in the natural-language sequence. The technology generates a masked natural-language sequence by masking a first entity in the natural-language sequence. The technology retrieves, from a knowledge base, information related to a second entity in the plurality of entities. The technology then trains a natural-language model to respond to a query. The training uses a first representation of the masked natural-language sequence, a second representation of the information, and the first entity.

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