Method for discovering causality from data, electronic device and storage medium

    公开(公告)号:US11947552B2

    公开(公告)日:2024-04-02

    申请号:US17947659

    申请日:2022-09-19

    CPC classification number: G06F16/2465 G06F16/2237

    Abstract: A method for discovering causality from data includes acquiring to-be-processed data, and obtaining a covariance matrix of the to-be-processed data; determining a first target column in the covariance matrix, taking the number of columns of the first target column as a first place in a rearrangement sequence, and obtaining a first upper triangular matrix according to the first target column; determining a position of the number of columns of the covariance matrix other than the first target column except the first place in the rearrangement sequence according to the first target column and the first upper triangular matrix, and obtaining an upper triangular matrix in each position determination; obtaining an adjacency matrix according to an upper triangular matrix and a rearrangement sequence obtained in final position determination; and generating directed acyclic graph (DAG) by using the adjacency matrix, and taking the DAG as causality discovery result of the to-be-processed data.

    METHOD FOR TRAINING CLICK RATE PREDICTION MODEL

    公开(公告)号:US20240104403A1

    公开(公告)日:2024-03-28

    申请号:US18521061

    申请日:2023-11-28

    CPC classification number: G06N5/022

    Abstract: A method for training a click rate prediction model includes: obtaining sample feature information and a label value, in which the sample feature information includes feature information of a sample user and feature information of a target object, and the label value is configured to indicate whether the sample user interacts with the target object; obtaining a plurality of adjacent matrixes for feature interaction by processing the feature information of the target object based on the hypernetwork module; obtaining a click rate prediction value of the sample user on the target object using the prediction module, according to the sample feature information and the plurality of adjacent matrixes; and training the click rate prediction model according to the label value and the click rate prediction value.

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    发明公开

    公开(公告)号:US20240104154A1

    公开(公告)日:2024-03-28

    申请号:US18016754

    申请日:2022-07-25

    Inventor: Zhou CHENG

    CPC classification number: G06F16/9538 G06N3/0455

    Abstract: A method is provided that includes: determining a plurality of recall data associated with data to be searched; determining, for each recall data of the plurality of recall data, a recommendation degree of the recall data based on a similarity between the recall data and each recall data of the plurality of recall data; and ranking the recall data in the plurality of recall data based on the recommendation degree of each recall data of the plurality of recall data.

    METHOD AND APPARATUS FOR SIMULATING QUANTUM CIRCUIT

    公开(公告)号:US20240070500A1

    公开(公告)日:2024-02-29

    申请号:US17899575

    申请日:2022-08-30

    Inventor: Kun Fang

    CPC classification number: G06N10/20

    Abstract: A method for operating a quantum computer is provided. The method includes: obtaining a quantum gate parameter of each quantum gate in a quantum circuit to be simulated; and generating a sub-measurement mode equivalent to the quantum gate based on the quantum gate parameter. The method further includes combining the sub-measurement mode equivalent to each quantum gate to obtain an overall measurement mode equivalent to the quantum circuit to be simulated as a whole; and sorting an operation order of the operation commands of each sub-measurement mode in the overall measurement mode, thereby obtaining the sorted overall measurement mode as a simulation result. This solution transforms the quantum circuit used for quantum computation into an equivalent measurement mode according to a one-way quantum computer computation model. Direct simulation computation on the quantum circuit can be avoided, and accordingly a simulation operation amount of a classical computer is greatly reduced.

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