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    Combinatorial Bayesian optimization using a graph cartesian product
    93.
    发明授权
    Combinatorial Bayesian optimization using a graph cartesian product 有权

    公开(公告)号：US11842279B2

    公开(公告)日：2023-12-12

    申请号：US16945625

    申请日：2020-07-31

    申请人： QUALCOMM Technologies, Inc.

    发明人： Changyong Oh ,  Efstratios Gavves ,  Jakub Mikolaj Tomczak ,  Max Welling

    IPC分类号： G06N3/082 ,  G06F18/20 ,  G06F18/10 ,  G06F18/21 ,  G06N7/01

    CPC分类号： G06N3/082 ,  G06F18/10 ,  G06F18/217 ,  G06F18/29 ,  G06N7/01

    摘要： Certain aspects provide a method for determining a solution to a combinatorial optimization problem, including: determining a plurality of subgraphs, wherein each subgraph of the plurality of subgraphs corresponds to a combinatorial variable of the plurality of combinatorial variables; determining a combinatorial graph based on the plurality of subgraphs; determining evaluation data comprising a set of vertices in the combinatorial graph and evaluations on the set of vertices; fitting a Gaussian process to the evaluation data; determining an acquisition function for vertices in the combinatorial graph using a predictive mean and a predictive variance from the fitted Gaussian process; optimizing the acquisition function on the combinatorial graph to determine a next vertex to evaluate; evaluating the next vertex; updating the evaluation data with a tuple of the next vertex and its evaluation; and determining a solution to the problem, wherein the solution comprises a vertex of the combinatorial graph.

    IDENTIFICATION METHOD AND INFORMATION PROCESSING DEVICE
    98.
    发明公开
    IDENTIFICATION METHOD AND INFORMATION PROCESSING DEVICE 审中-公开

    公开(公告)号：US20230281275A1

    公开(公告)日：2023-09-07

    申请号：US18092948

    申请日：2023-01-04

    申请人： Fujitsu Limited

    发明人： Akira URA

    IPC分类号： G06F18/10 ,  G06N20/00

    CPC分类号： G06F18/10 ,  G06N20/00

    摘要： A non-transitory computer-readable recording medium stores a program for causing a computer to execute a process, the process includes obtaining first change information, which indicates a change in a feature of a first dataset when first preprocessing is performed on the first dataset, inputting the first change information to a trained machine learning model that outputs an inference result regarding preprocessing information that identifies each piece of second preprocessing for a second dataset, the trained machine learning model being trained by using training data in which the preprocessing information is associated with second change information that indicates a change in a feature of the second dataset when each piece of second preprocessing is performed, and identifying one or more pieces of recommended preprocessing that correspond to the first preprocessing based on the inference result that is output in response to the input of the first change information.