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    Method and System for Adversarial Training and for Analyzing Impact of Fine-Tuning on Deep Learning Models
    1.
    发明申请
    Method and System for Adversarial Training and for Analyzing Impact of Fine-Tuning on Deep Learning Models 有权

    公开(公告)号：US20250124298A1

    公开(公告)日：2025-04-17

    申请号：US18292244

    申请日：2022-07-29

    Applicant: Visa International Service Association

    Inventor： Javid Ebrahimi ,  Wei Zhang ,  Hao Yang

    IPC: G06N3/094 ,  G06N3/045

    Abstract: Methods for adversarial training and/or for analyzing the impact of fine-tuning on deep learning models may include receiving a deep learning model comprising a set of parameters and a dataset of samples. A respective noise vector for a respective sample may be generated based on a length of the sample and a radius hyperparameter. For a target number of steps, the following may be repeated: adjusting the noise vector based on a step size hyperparameter, and projecting the respective noise vector to be within a boundary. The parameters of the deep learning model may be adjusted based on a gradient of a loss based on the noise vector. This may be repeated for each sample of the plurality of samples. A system and computer program product are also disclosed.

    System, Method, Computer Program Product for Operating a Gated Multilayer Perceptron Machine Learning Model Architecture
    2.
    发明申请
    System, Method, Computer Program Product for Operating a Gated Multilayer Perceptron Machine Learning Model Architecture 有权

    公开(公告)号：US20250045621A1

    公开(公告)日：2025-02-06

    申请号：US18229272

    申请日：2023-08-02

    Applicant: Visa International Service Association

    Inventor： Yuzhong Chen ,  Mahashweta Das ,  Hao Yang

    IPC: G06N20/00

    Abstract: Provided is a system that includes a processor to receive interaction data associated with a plurality of interactions, generate a first intermediate embedding, a second intermediate embedding, and a third intermediate embedding using at least one machine learning model, provide the first intermediate embedding as an input to a gating machine learning model to generate an intermediate classification of the first intermediate embedding, multiply the intermediate classification of the first intermediate embedding, the second intermediate embedding, and the third intermediate embedding to provide an intermediate product of outputs, combine the first intermediate embedding and the intermediate product of outputs to provide a combined final input, and generate an output classification label of the combined final input based on providing the combined final input to a head machine learning model. Methods and computer program products are also provided.

    Method and system for assessing the reputation of a merchant
    4.
    发明授权
    Method and system for assessing the reputation of a merchant 有权

    公开(公告)号：US12141807B2

    公开(公告)日：2024-11-12

    申请号：US17763255

    申请日：2019-10-31

    Applicant: VISA INTERNATIONAL SERVICE ASSOCIATION

    Inventor： Liang Wang ,  Dhruv Gelda ,  Robert Christensen ,  Wei Zhang ,  Hao Yang ,  Yan Zheng

    IPC: G06Q20/40 ,  G06Q30/018

    Abstract: The system and method may assess the merchant risk level on a more continuous scale rather than a binary categorization. It may produce a continuous risk score proportional to the likelihood of a merchant being risky, effectively addressing the issue of shades of gray encountered by the traditional blacklisting approach. The continuous risk score feature provides greater flexibility as it allows the payment network to make dynamic pricing decisions (known as interchange optimization) based on the merchant risk level. Using collective intelligence from transactions across the payment network, the system and method may be able to assess the merchant risk level with high accuracy. The system and method may be particularly beneficial to small merchants with low transaction volume as even a few fraudulent transactions can easily put them in the high-risk merchant category. Further, the system and method may help payment processing networks make better decision on cross-border transactions.

    System, Method, and Computer Program Product for Generating Embeddings for Objects
    5.
    发明公开
    System, Method, and Computer Program Product for Generating Embeddings for Objects 审中-公开

    公开(公告)号：US20240354733A1

    公开(公告)日：2024-10-24

    申请号：US18760640

    申请日：2024-07-01

    Applicant: Visa International Service Association

    Inventor： Manoj Reddy Dareddy ,  Mahashweta Das ,  Hao Yang

    IPC: G06Q20/30

    CPC classification number: G06Q20/30

    Abstract: Provided are computer-implemented methods for generating embeddings for objects which may include receiving heterogeneous network data associated with a plurality of objects in a heterogeneous network; selecting at least one pattern of objects; determining instances of each pattern of objects based on the heterogeneous network data; generating a pattern matrix for each pattern of objects based on the instances of the pattern of objects; generating pattern sequence data associated with a portion of each pattern matrix; generating network sequence data associated with a portion of the heterogeneous network data; and combining the pattern sequence data and the network sequence data into combined sequence data. In some non-limiting embodiments or aspects, methods may include generating a vector for each object of the plurality of objects based on the combined sequence data. Systems and computer program products are also provided.

    Mixed-initiative machine learning systems and methods for determining segmentations
    6.
    发明授权
    Mixed-initiative machine learning systems and methods for determining segmentations 有权

    公开(公告)号：US12118439B2

    公开(公告)日：2024-10-15

    申请号：US17326688

    申请日：2021-05-21

    Applicant: Visa International Service Association

    Inventor： Liang Gou ,  Hao Yang

    IPC: G06N5/04 ,  G06N20/00 ,  G06Q30/0204

    CPC classification number: G06N20/00 ,  G06N5/04 ,  G06Q30/0204

    Abstract: A computer system can perform a semi-supervised machine learning processes to cluster a plurality of entities within a population based on their features and associated labels. The computer system can generate visualization data representing the clusters of entities and associated labels for displaying on a user interface. A user can review the clustering of entities and use the user interface to add or modify the labels associated with a particular entity or set of entities. The computer system can use the user's feedback to update the labels and then re-determine the clustering of entities using the semi-supervised machine learning process with the updated labels as input. As such, the computer system can use the user's feedback to improve the accuracy of the machine learning model without requiring a larger amount of labeled input data.

    System, method, and computer program product for generating embeddings for objects
    8.
    发明授权
    System, method, and computer program product for generating embeddings for objects 有权

    公开(公告)号：US12039513B2

    公开(公告)日：2024-07-16

    申请号：US17297000

    申请日：2019-12-02

    Applicant: Visa International Service Association

    Inventor： Manoj Reddy Dareddy ,  Mahashweta Das ,  Hao Yang

    IPC: G06Q20/30

    CPC classification number: G06Q20/30

    Abstract: Provided are computer-implemented methods for generating embeddings for objects which may include receiving heterogeneous network data associated with a plurality of objects in a heterogeneous network; selecting at least one pattern of objects; determining instances of each pattern of objects based on the heterogeneous network data; generating a pattern matrix for each pattern of objects based on the instances of the pattern of objects; generating pattern sequence data associated with a portion of each pattern matrix; generating network sequence data associated with a portion of the heterogeneous network data; and combining the pattern sequence data and the network sequence data into combined sequence data. In some non-limiting embodiments or aspects, methods may include generating a vector for each object of the plurality of objects based on the combined sequence data. Systems and computer program products are also provided.

    Method, System, and Computer Program Product for Generating Robust Graph Neural Networks Using Universal Adversarial Training
    10.
    发明公开
    Method, System, and Computer Program Product for Generating Robust Graph Neural Networks Using Universal Adversarial Training 审中-公开

    公开(公告)号：US20240095526A1

    公开(公告)日：2024-03-21

    申请号：US18286799

    申请日：2023-02-17

    Applicant: Visa International Service Association

    Inventor： Huiyuan Chen ,  Fei Wang ,  Hao Yang

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: Described are a method, system, and computer program product for generating robust graph neural networks using universal adversarial training. The method includes receiving a graph neural network (GNN) model and a bipartite graph including an adjacency matrix, initializing model parameters of the GNN model, initializing perturbation parameters, and sampling a subgraph of a complementary graph based on the bipartite graph. The method further includes repeating until convergence of the model parameters: drawing a random variable from a uniform distribution; generating a universal perturbation matrix based on the subgraph, the random variable, and the perturbation parameters; determining Bayesian Personalized Ranking (BPR) loss by inputting the bipartite graph and the universal perturbation matrix to the GNN model; updating the perturbation parameters based on stochastic gradient ascent; and updating the model parameters based on stochastic gradient descent. The method further includes, in response to convergence of the model parameters, outputting the model parameters.

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