Method and Apparatus for Generating and Applying Deep Learning Model based on Deep Learning Framework

    公开(公告)号:US20230185702A1

    公开(公告)日:2023-06-15

    申请号:US17856091

    申请日:2022-07-01

    CPC classification number: G06F11/3688 G06N3/08

    Abstract: A method and apparatus is provided for generating and applying a deep learning model based on a deep learning framework, and relates to the field of computers. A specific implementation solution includes that a basic operating environment is established on a target device, where the basic operating environment is used for providing environment preparation for an overall generation process of a deep learning model; a basic function of the deep learning model is generated in the basic operating environment according to at least one of a service requirement and a hardware requirement, to obtain a first processing result; an extended function of the deep learning model is generated in the basic operating environment based on the first processing result, to obtain a second processing result; and a preset test script is used to perform function test on the second processing result, to output a test result.

    METHOD FOR DETERMINING DRAWING DATA OF MAP ELEMENT, ELECTRONIC DEVICE, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM

    公开(公告)号:US20230177749A1

    公开(公告)日:2023-06-08

    申请号:US18077032

    申请日:2022-12-07

    CPC classification number: G06T11/203

    Abstract: Provided are a method for determining drawing data of a map element, an electronic device, and a non-transitory computer-readable storage medium. The specific implementation scheme includes determining a reference point position of a non-link element according to a relationship between the non-link element and adjacent link elements in an electronic map and based on lane edge positions of the adjacent link elements; determining a direction of the non-link element according to the relationship between the non-link element and the adjacent link elements; and determining drawing data of the non-link element according to the reference point position and the direction of the non-link element, where the drawing data is used for drawing and rendering an image of the non-link element in a link when the drawing data is loaded by a map interface.

    QUANTUM CHIP AND CONSTRUCTION METHOD AND CONSTRUCTION APPARATUS THEREOF

    公开(公告)号:US20230172076A1

    公开(公告)日:2023-06-01

    申请号:US18095994

    申请日:2023-01-11

    CPC classification number: H10N60/12 G06N10/40 H10N60/805

    Abstract: A quantum chip is provided, includes: a first substrate and a second substrate arranged opposite to each other, wherein a plurality of qubits and a plurality of first controllers are arranged on a surface of the first substrate facing the second substrate, each of the plurality of qubits is coupled with at least one of the plurality of first controllers, and a plurality of control signal transmission parts are arranged on a surface of the second substrate facing the first substrate; and a plurality of connecting pieces, connected between the first substrate and the second substrate, and configured to connect the plurality of first controllers to the plurality of control signal transmission parts in a one-to-one corresponding mode.

    TECHNIQUES FOR MODEL TRAINING
    430.
    发明公开

    公开(公告)号:US20230162722A1

    公开(公告)日:2023-05-25

    申请号:US18052155

    申请日:2022-11-02

    Inventor: Chao LI

    CPC classification number: G10L15/063 G06N5/022 G10L15/22 G10L15/1815 G10L15/30

    Abstract: A model training method is provided. An implementation solution is: determining reference classification and reference confidence of sample data, wherein the reference classification and the reference confidence are obtained by utilizing a plurality of classifiers to classify the sample data; inputting the sample data into a to-be-trained model to obtain a first predicted classification and a first confidence output by the to-be-trained model; and adjusting parameters of the to-be-trained model based on at least the reference classification and the reference confidence of the sample data, and the first predicted classification and the first confidence.

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