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    System, Method, and Computer Program Product for Determining Long-Range Dependencies Using a Non-Local Graph Neural Network (GNN)
    1.
    发明申请
    System, Method, and Computer Program Product for Determining Long-Range Dependencies Using a Non-Local Graph Neural Network (GNN) 有权

    公开(公告)号：US20240419939A1

    公开(公告)日：2024-12-19

    申请号：US18702960

    申请日：2022-10-20

    Applicant: Visa International Service Association

    Inventor： Huiyuan Chen ,  Michael Yeh ,  Fei Wang ,  Hao Yang

    IPC: G06N3/042 ,  G06N3/0464

    Abstract: Systems, methods, and computer program products for determining long-range dependencies using a non-local graph neural network (GNN): receive a dataset comprising historical data; generate at least one layer of a graph neural network by generating graph convolutions to compute node embeddings for a plurality of nodes of the dataset, the graph convolutions generated by aggregating node data from a first node of the dataset and node data from at least one second node comprising a neighbor node of the first node; cluster the node embeddings to form a plurality of centroids; determine an attention operator for at least one node-centroid pairing, the at least one node-centroid pairing comprising the first node and a first centroid; and generate relational data corresponding to a relation between the first node and at least one third node comprising a non-neighbor node of the first node using the attention operator.

    System, Method, and Computer Program Product for Denoising Sequential Machine Learning Models
    2.
    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.

    ERROR-BOUNDED APPROXIMATE TIME SERIES JOIN USING COMPACT DICTIONARY REPRESENTATION OF TIME SERIES
    6.
    发明公开
    ERROR-BOUNDED APPROXIMATE TIME SERIES JOIN USING COMPACT DICTIONARY REPRESENTATION OF TIME SERIES 审中-公开

    公开(公告)号：US20240273095A1

    公开(公告)日：2024-08-15

    申请号：US18567717

    申请日：2022-06-01

    Applicant: Visa International Service Association

    Inventor： Michael Yeh ,  Yan Zheng ,  Junpeng Wang ,  Wei Zhang ,  Zhongfang Zhuang

    IPC: G06F16/2453 ,  G06F16/2458

    CPC classification number: G06F16/24537 ,  G06F16/2465 ,  G06F16/2477

    Abstract: A method is disclosed. The method comprises determining a time series, a subsequence length. The length of the time series may then be determined, and an initial matrix profile may then be computed. The method may then form a processed matrix profile for a first subsequence of the subsequence length by applying the first subsequence to the initial matrix profile. A second subsequence may then be determined from the processed matrix profile. The method may then include comparing the second subsequence to other subsequences in a dictionary and adding it to the dictionary. The subsequences in the dictionary may be used to generate a plurality of subsequence matrix profiles. The method may then include forming an approximate matrix profile using the plurality of subsequence matrix profiles and then determining one or more anomalies in the time series or another time series using the approximate matrix profile.

    System, Method, and Computer Program Product for Debiasing Embedding Vectors of Machine Learning Models
    7.
    发明公开
    System, Method, and Computer Program Product for Debiasing Embedding Vectors of Machine Learning Models 审中-公开

    公开(公告)号：US20240160854A1

    公开(公告)日：2024-05-16

    申请号：US18280792

    申请日：2022-03-30

    Applicant: Visa International Service Association

    Inventor： Sunipa Dev ,  Yan Zheng ,  Michael Yeh ,  Junpeng Wang ,  Wei Zhang ,  Archit Rathore

    IPC: G06F40/40

    CPC classification number: G06F40/40

    Abstract: Described are a system, method, and computer program product for debiasing embedding vectors of machine learning models. The method includes receiving embedding vectors and generating two clusters thereof. The method includes determining a first mean vector of the first cluster and a second mean vector of the second cluster. The method includes determining a bias associated with each of a plurality of first candidate vectors and replacing the first mean vector with a first candidate vector based on the bias. The method includes determining a bias associated with each of a plurality of second candidate vectors and replacing the second mean vector with a second candidate vector based on the bias. The method includes repeatedly replacing the first and second mean vectors until an extremum of the bias score is reached, and debiasing the embedding vectors by linear projection using a direction defined by the first and second mean vectors.

    System, Method, and Computer Program Product for Dynamic Node Classification in Temporal-Based Machine Learning Classification Models
    9.
    发明公开
    System, Method, and Computer Program Product for Dynamic Node Classification in Temporal-Based Machine Learning Classification Models 审中-公开

    公开(公告)号：US20240078416A1

    公开(公告)日：2024-03-07

    申请号：US18271301

    申请日：2023-01-30

    Applicant: Visa International Service Association

    Inventor： Jiarui Sun ,  Mengting Gu ,  Michael Yeh ,  Liang Wang ,  Wei Zhang

    IPC: G06N3/049 ,  G06F17/16

    CPC classification number: G06N3/049 ,  G06F17/16

    Abstract: Described are a system, method, and computer program product for dynamic node classification in temporal-based machine learning classification models. The method includes receiving graph data of a discrete time dynamic graph including graph snapshots, and node classifications associated with all nodes in the discrete time dynamic graph. The method includes converting the discrete time dynamic graph to a time-augmented spatio-temporal graph and generating an adjacency matrix based on a temporal walk of the time-augmented spatio-temporal graph. The method includes generating an adaptive information transition matrix based on the adjacency matrix and determining feature vectors based on the nodes and the node attribute matrix of each graph snapshot. The method includes generating and propagating initial node representations across information propagation layers using the adaptive information transition matrix and classifying a node of the discrete time dynamic graph subsequent to the first time period based on final node representations.

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