公开(公告)号：US20250103884A1

公开(公告)日：2025-03-27

申请号：US18889563

申请日：2024-09-19

Applicant: Visa International Service Association

Inventor： Yujie Fan ,  Jiarui Sun ,  Michael Yeh ,  Wei Zhang

IPC: G06N3/08 ,  G06N3/0455

Abstract: Methods, systems, and computer program products are provided for spatial-temporal prediction using trained spatial-temporal masked autoencoders. An example system includes a processor configured to determine a structural dependency graph associated with a networked system. The processor is also configured to receive multivariate time-series data from a first time period associated with the networked system. The processor is further configured to mask the plurality of edges of the structural dependency graph and mask the multivariate time-series data. The processor is further configured to train a spatial-temporal autoencoder based on the masked structural representation and the masked temporal representation. The processor is further configured to generate a prediction using a spatial-temporal machine learning model including the trained spatial-temporal autoencoder, the prediction associated with an attribute of the networked system in a second time period subsequent to the first time period.

公开(公告)号：US20240256863A1

公开(公告)日：2024-08-01

申请号：US18426717

申请日：2024-01-30

Applicant: Visa International Service Association

Inventor： Huiyuan Chen ,  Mahashweta Das ,  Michael Yeh ,  Yujie Fan ,  Yan Zheng ,  Junpeng Wang ,  Vivian Wan Yin Lai ,  Hao Yang

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and computer program products are provided for optimizing training loss of a graph neural network machine learning model using bi-level optimization. An example method includes receiving a training dataset comprising graph data associated with a graph, training a graph neural network (GNN) machine learning model using a loss equation according to a bi-level optimization problem and based on the training dataset, where training the GNN machine learning model using the loss equation according to the bi-level optimization problem includes determining a solution to an inner loss problem and a solution to an outer loss problem, and providing a trained GNN machine learning model based on training the GNN machine learning model.