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    STRUCTURED GRAPH CONVOLUTIONAL NETWORKS WITH STOCHASTIC MASKS FOR NETWORK EMBEDDINGS
    3.
    发明公开
    STRUCTURED GRAPH CONVOLUTIONAL NETWORKS WITH STOCHASTIC MASKS FOR NETWORK EMBEDDINGS 审中-公开

    公开(公告)号：US20240046075A1

    公开(公告)日：2024-02-08

    申请号：US18264052

    申请日：2021-07-02

    Applicant: VISA INTERNATIONAL SERVICE ASSOCIATION

    Inventor： Huiyuan Chen ,  Yu-San Lin ,  Lan Wang ,  Michael Yeh ,  Fei Wang ,  Hao Yang

    IPC: G06N3/0464 ,  G06N3/047

    CPC classification number: G06N3/0464 ,  G06N3/047 ,  G06Q30/0282

    Abstract: A method includes receiving a first data set comprising embeddings of first and second types, generating a fixed adjacency matrix from the first dataset, and applying a first stochastic binary mask to the fixed adjacency matrix to obtain a first subgraph of the fixed adjacency matrix. The method also includes processing the first subgraph through a first layer of a graph convolutional network (GCN) to obtain a first embedding matrix, and applying a second stochastic binary mask to the fixed adjacency matrix to obtain a second subgraph of the fixed adjacency matrix. The method includes processing the first embedding matrix and the second subgraph through a second layer of the GCN to obtain a second embedding matrix, and then determining a plurality of gradients of a loss function, and modifying the first stochastic binary mask and the second stochastic binary mask using at least one of the plurality of gradients.

    System, Method, and Computer Program Product for Denoising Sequential Machine Learning Models
    5.
    发明申请
    System, Method, and Computer Program Product for Denoising Sequential Machine Learning Models 有权

    公开(公告)号：US20240412065A1

    公开(公告)日：2024-12-12

    申请号：US18702382

    申请日：2022-09-30

    Applicant: Visa International Service Association

    Inventor： Huiyuan Chen ,  Yu-San Lin ,  Menghai Pan ,  Lan Wang ,  Michael Yeh ,  Fei Wang ,  Hao Yang

    IPC: G06N3/08

    Abstract: Described are a system, method, and computer program product for denoising sequential machine learning models. The method includes receiving data associated with a plurality of sequences and training a sequential machine learning model based on the data associated with the plurality of sequences to produce a trained sequential machine learning model. Training the sequential machine learning model includes denoising a plurality of sequential dependencies between items in the plurality of sequences using at least one trainable binary mask. The method also includes generating an output of the trained sequential machine learning model based on the denoised sequential dependencies. The method further includes generating a prediction of an item associated with a sequence of items based on the output of the trained sequential machine learning model.

    HIERARCHICAL PERIODICITY DETECTION ON DYNAMIC GRAPHS SYSTEM AND METHOD
    6.
    发明公开
    HIERARCHICAL PERIODICITY DETECTION ON DYNAMIC GRAPHS SYSTEM AND METHOD 审中-公开

    公开(公告)号：US20240289355A1

    公开(公告)日：2024-08-29

    申请号：US18568778

    申请日：2022-06-10

    Applicant: VISA INTERNATIONAL SERVICE ASSOCIATION

    Inventor： Yu-San Lin ,  Lan Wang ,  Yuhang Wu ,  Huiyuan Chen ,  Fei Wang ,  Hao Yang

    IPC: G06F16/28

    CPC classification number: G06F16/285

    Abstract: A computer obtains node embeddings, node periodicity classifications, edge embeddings, and edge periodicity classifications for each time of a time period. The computer determines subgraph embeddings based on a subgraph of the graph, times in the time period, the node embeddings for nodes in the subgraph, the edge embeddings for edges in the subgraph, the node periodicity classifications for the nodes in the subgraph, and the edge periodicity classifications for the edges in the subgraph. The computer translates each subgraph embedding of the subgraph embeddings for each time of the time period into projected subgraph embeddings. For the subgraph, the computer aggregates the plurality of projected subgraph embeddings into an aggregated subgraph embedding. The computer determines if the subgraph is periodic based upon at least the aggregated subgraph embedding.

    System, Method, and Computer Program Product for Anomaly Detection in Multivariate Time Series
    8.
    发明公开
    System, Method, and Computer Program Product for Anomaly Detection in Multivariate Time Series 审中-公开

    公开(公告)号：US20240152735A1

    公开(公告)日：2024-05-09

    申请号：US18280727

    申请日：2022-06-10

    Applicant: Visa International Service Association

    Inventor： Lan Wang ,  Yu-San Lin ,  Yuhang Wu ,  Huiyuan Chen ,  Fei Wang ,  Hao Yang

    IPC: G06N3/0464

    CPC classification number: G06N3/0464

    Abstract: Provided is a system for detecting an anomaly in a multivariate time series that includes at least one processor programmed or configured to receive a dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, determine a set of target data instances based on the dataset, determine a set of historical data instances based on the dataset, generate, based on the set of target data instances, a true value matrix, a true frequency matrix, and a true correlation matrix, generate a forecast value matrix, a forecast frequency matrix, and a forecast correlation matrix based on the set of target data instances and the set of historical data instances, determine an amount of forecasting error, and determine whether the amount of forecasting error corresponds to an anomalous event associated with the dataset of data instances. Methods and computer program products are also provided.

    POLICY-GUIDED DOMAIN ADAPTATION FOR ANOMALY DETECTION
    9.
    发明公开
    POLICY-GUIDED DOMAIN ADAPTATION FOR ANOMALY DETECTION 审中-公开

    公开(公告)号：US20230367760A1

    公开(公告)日：2023-11-16

    申请号：US17742966

    申请日：2022-05-12

    Applicant: Visa International Service Association

    Inventor： Kwei-Herng Lai ,  Lan Wang ,  Huiyuan Chen ,  Fei Wang ,  Hao Yang

    IPC: G06F16/23 ,  G06N3/08

    CPC classification number: G06F16/2365 ,  G06N3/08

    Abstract: Embodiments are directed to novel techniques for performing domain adaptation on time series data. Using embodiments, labeled source time series data can be used in order to label unlabeled target time series data as either normal or anomalous. Embodiments can accomplish this using an anomaly detector system comprising an anomaly detector component and a context sampler component. The context sampler can determine source and target window sizes used to sample data from the source and target data sets respectively. These samples can be input into the anomaly detector, which can label a target data value corresponding to the target sample as normal or anomalous. The anomaly detector can additionally generate a state value, which can be used by the context sampler to adjust the source and target window sizes accordingly. In this way, embodiments can accurately and automatically perform domain adaptation.

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