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    Personalized automated machine learning
    2.
    发明授权
    Personalized automated machine learning 有权

    公开(公告)号：US11379710B2

    公开(公告)日：2022-07-05

    申请号：US16805019

    申请日：2020-02-28

    Applicant: International Business Machines Corporation

    Inventor： Dakuo Wang ,  Chuang Gan ,  Ming Tan ,  Arunima Chaudhary ,  Lin Ju

    IPC: G06N3/04 ,  G06K9/62 ,  G06N3/08

    Abstract: In accordance with an embodiment of the invention, a method is provided for personalizing machine learning models for users of an automated machine learning system, the machine learning models being generated by an automated machine learning system. The method includes obtaining a first set of datasets for training first, second, and third neural networks, inputting the training datasets to the neural networks, tuning hyperparameters for the first, second, and third neural networks for testing and training the neural networks, inputting a second set of datasets to the trained neural networks and the third neural network generating a third output data including a relevance score for each of the users for each of the machine learning models, and displaying a list of machine learning models associated with each of the users, with each of the machine learning models showing the relevance score.

    Code generation for Auto-AI
    6.
    发明授权
    Code generation for Auto-AI 有权

    公开(公告)号：US11861469B2

    公开(公告)日：2024-01-02

    申请号：US16919258

    申请日：2020-07-02

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Peter Daniel Kirchner ,  Gregory Bramble ,  Horst Cornelius Samulowitz ,  Dakuo Wang ,  Arunima Chaudhary ,  Gregory Filla

    IPC: G06N20/00 ,  G06F8/35 ,  G06F8/76

    CPC classification number: G06N20/00 ,  G06F8/35 ,  G06F8/76

    Abstract: An embodiment of the invention may include a method, computer program product, and system for creating a data analysis tool. The method may include a computing device that generates an AI pipeline based on an input dataset, wherein the AI pipeline is generated using an Automated Machine Learning program. The method may include converting the AI pipeline to a non-native format of the Automated Machine Learning program. This may enable the AI pipeline to be used outside of the Automated Machine Learning program, thereby increasing the usefulness of the created program by not tying it to the Automated Machine Learning program. Additionally, this may increase the efficiency of running the AI pipeline by eliminating unnecessary computations performed by the Automated Machine Learning program.

    CONDITIONAL PARALLEL COORDINATES IN AUTOMATED ARTIFICIAL INTELLIGENCE WITH CONSTRAINTS
    9.
    发明申请
    CONDITIONAL PARALLEL COORDINATES IN AUTOMATED ARTIFICIAL INTELLIGENCE WITH CONSTRAINTS 有权

    公开(公告)号：US20210304028A1

    公开(公告)日：2021-09-30

    申请号：US16832528

    申请日：2020-03-27

    Applicant: International Business Machines Corporation

    Inventor： Daniel Karl I. Weidele ,  Parikshit Ram ,  Dakuo Wang ,  Abel Nicolas Valente ,  Arunima Chaudhary

    IPC: G06N5/04 ,  G06N20/00

    Abstract: Systems, computer-implemented methods, and computer program products to facilitate conditional parallel coordinates in automated artificial intelligence with constraints are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a visualization component that renders a pipeline constraint as a constraint axis having constraint scores of machine learning pipelines in a conditional parallel coordinates visualization. The computer executable components can further comprise a model generation component that generates a machine learning model based on the constraint scores of the machine learning pipelines.

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