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    Transfer learning for molecular structure generation
    3.
    发明授权
    Transfer learning for molecular structure generation 有权

    公开(公告)号：US12079730B2

    公开(公告)日：2024-09-03

    申请号：US16886160

    申请日：2020-05-28

    Applicant: International Business Machines Corporation

    Inventor： Enara C Vijil ,  Payel Das ,  Inkit Padhi

    IPC: G06N3/088 ,  G06N3/045 ,  G06N3/047 ,  G16C20/70

    CPC classification number: G06N3/088 ,  G06N3/045 ,  G06N3/047 ,  G16C20/70

    Abstract: Techniques regarding generating molecular structures with attributes of interest are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a transfer learning component that determines a molecular structure of a compound by employing a transfer learning process that utilizes lessons learned from an unconditional generative machine learning model to train a conditional machine learning model that regards a target attribute profile.

    TRANSFER LEARNING FOR MOLECULAR STRUCTURE GENERATION
    6.
    发明申请
    TRANSFER LEARNING FOR MOLECULAR STRUCTURE GENERATION 有权

    公开(公告)号：US20210374551A1

    公开(公告)日：2021-12-02

    申请号：US16886160

    申请日：2020-05-28

    Applicant: International Business Machines Corporation

    Inventor： Enara C Vijil ,  Payel Das ,  Inkit Padhi

    IPC: G06N3/08 ,  G06N20/00 ,  G06K9/62

    Abstract: Techniques regarding generating molecular structures with attributes of interest are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a transfer learning component that determines a molecular structure of a compound by employing a transfer learning process that utilizes lessons learned from an unconditional generative machine learning model to train a conditional machine learning model that regards a target attribute profile.

    Supervised environment controllable auto-generation of HTML
    8.
    发明授权
    Supervised environment controllable auto-generation of HTML 有权

    公开(公告)号：US11093217B2

    公开(公告)日：2021-08-17

    申请号：US16701463

    申请日：2019-12-03

    Applicant: International Business Machines Corporation

    Inventor： Abhijit Mishra ,  Enara C Vijil ,  Seema Nagar ,  Kuntal Dey

    IPC: G06F9/44 ,  G06F8/30 ,  G06F16/951 ,  G06N3/08 ,  G06F8/60 ,  G06N3/04 ,  G06F8/20

    Abstract: In an approach to generating HTML based on a plurality of content and design controls, one or more computer processors crawl one or more conforming websites. The one or more computer processors create a training set of crawled conforming webpages, wherein each crawled conforming webpage in the training set of crawled conforming webpages includes associated web code labeled a combination of respective one or more content controls and respective one or more design controls; encode the combination of the respective one or more design controls and the respective one or more content controls based on one or more user preferences utilizing a created design encoder and a created content encoder; create a decoder with the training set of crawled conforming webpages and associated encoded content and design vectors; generate web code based on the encoded design and content controls utilizing the created decoder; implement generated web code.

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