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    DEEP NEURAL NETWORK SYSTEM FOR SIMILARITY-BASED GRAPH REPRESENTATIONS
    2.
    发明申请
    DEEP NEURAL NETWORK SYSTEM FOR SIMILARITY-BASED GRAPH REPRESENTATIONS 有权

    公开(公告)号：US20230134742A1

    公开(公告)日：2023-05-04

    申请号：US18087704

    申请日：2022-12-22

    Applicant: DeepMind Technologies Limited

    Inventor： Yujia Li ,  Chenjie Gu ,  Thomas Dullien ,  Oriol Vinyals ,  Pushmeet Kohli

    IPC: G06F21/56 ,  G06F21/57 ,  G06N3/04 ,  G06F17/16 ,  G06F16/901 ,  G06F18/22 ,  G06V30/196 ,  G06V10/82 ,  G06V10/426

    Abstract: There is described a neural network system implemented by one or more computers for determining graph similarity. The neural network system comprises one or more neural networks configured to process an input graph to generate a node state representation vector for each node of the input graph and an edge representation vector for each edge of the input graph; and process the node state representation vectors and the edge representation vectors to generate a vector representation of the input graph. The neural network system further comprises one or more processors configured to: receive a first graph; receive a second graph; generate a vector representation of the first graph; generate a vector representation of the second graph; determine a similarity score for the first graph and the second graph based upon the vector representations of the first graph and the second graph.

    POPULATION-BASED TRAINING OF MACHINE LEARNING MODELS
    3.
    发明申请
    POPULATION-BASED TRAINING OF MACHINE LEARNING MODELS 有权

    公开(公告)号：US20210097443A1

    公开(公告)日：2021-04-01

    申请号：US16586236

    申请日：2019-09-27

    Applicant: DeepMind Technologies Limited

    Inventor： Ang Li ,  Valentin Clement Dalibard ,  David Budden ,  Ola Spyra ,  Maxwell Elliot Jaderberg ,  Timothy James Alexander Harley ,  Sagi Perel ,  Chenjie Gu ,  Pramod Gupta

    IPC: G06N20/20 ,  G06N5/04 ,  G06F16/901

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. A method includes: maintaining a plurality of training sessions; assigning, to each worker of one or more workers, a respective training session of the plurality of training sessions; repeatedly performing operations until meeting one or more termination criteria, the operations comprising: receiving an updated training session from a respective worker of the one or more workers, selecting a second training session, selecting, based on comparing the updated training session and the second training session using a fitness evaluation function, either the updated training session or the second training session as a parent training session, generating a child training session from the selected parent training session, and assigning the child training session to an available worker, and selecting a candidate model to be a trained model for the machine learning model.

    DEEP NEURAL NETWORK SYSTEM FOR SIMILARITY-BASED GRAPH REPRESENTATIONS
    4.
    发明申请
    DEEP NEURAL NETWORK SYSTEM FOR SIMILARITY-BASED GRAPH REPRESENTATIONS 审中-公开

    公开(公告)号：US20190354689A1

    公开(公告)日：2019-11-21

    申请号：US16416070

    申请日：2019-05-17

    Applicant: DeepMind Technologies Limited

    Inventor： Yujia Li ,  Chenjie Gu ,  Thomas Dullien ,  Oriol Vinyals ,  Pushmeet Kohli

    IPC: G06F21/57 ,  G06N3/04 ,  G06K9/62 ,  G06F16/901 ,  G06F17/16

    Abstract: There is described a neural network system implemented by one or more computers for determining graph similarity. The neural network system comprises one or more neural networks configured to process an input graph to generate a node state representation vector for each node of the input graph and an edge representation vector for each edge of the input graph; and process the node state representation vectors and the edge representation vectors to generate a vector representation of the input graph. The neural network system further comprises one or more processors configured to: receive a first graph; receive a second graph; generate a vector representation of the first graph; generate a vector representation of the second graph; determine a similarity score for the first graph and the second graph based upon the vector representations of the first graph and the second graph.

    Population-based training of machine learning models
    6.
    发明授权
    Population-based training of machine learning models 有权

    公开(公告)号：US11907821B2

    公开(公告)日：2024-02-20

    申请号：US16586236

    申请日：2019-09-27

    Applicant: DeepMind Technologies Limited

    Inventor： Ang Li ,  Valentin Clement Dalibard ,  David Budden ,  Ola Spyra ,  Maxwell Elliot Jaderberg ,  Timothy James Alexander Harley ,  Sagi Perel ,  Chenjie Gu ,  Pramod Gupta

    IPC: G06N20/20 ,  G06F16/901 ,  G06N5/04

    CPC classification number: G06N20/20 ,  G06F16/9024 ,  G06N5/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. A method includes: maintaining a plurality of training sessions; assigning, to each worker of one or more workers, a respective training session of the plurality of training sessions; repeatedly performing operations until meeting one or more termination criteria, the operations comprising: receiving an updated training session from a respective worker of the one or more workers, selecting a second training session, selecting, based on comparing the updated training session and the second training session using a fitness evaluation function, either the updated training session or the second training session as a parent training session, generating a child training session from the selected parent training session, and assigning the child training session to an available worker, and selecting a candidate model to be a trained model for the machine learning model.

    Deep neural network system for similarity-based graph representations
    8.
    发明授权
    Deep neural network system for similarity-based graph representations 有权

    公开(公告)号：US11537719B2

    公开(公告)日：2022-12-27

    申请号：US16416070

    申请日：2019-05-17

    Applicant: DeepMind Technologies Limited

    Inventor： Yujia Li ,  Chenjie Gu ,  Thomas Dullien ,  Oriol Vinyals ,  Pushmeet Kohli

    IPC: G08B23/00 ,  G06F12/16 ,  G06F12/14 ,  G06F11/00 ,  G06F21/57 ,  G06N3/04 ,  G06F17/16 ,  G06F16/901 ,  G06K9/62

    Abstract: There is described a neural network system implemented by one or more computers for determining graph similarity. The neural network system comprises one or more neural networks configured to process an input graph to generate a node state representation vector for each node of the input graph and an edge representation vector for each edge of the input graph; and process the node state representation vectors and the edge representation vectors to generate a vector representation of the input graph. The neural network system further comprises one or more processors configured to: receive a first graph; receive a second graph; generate a vector representation of the first graph; generate a vector representation of the second graph; determine a similarity score for the first graph and the second graph based upon the vector representations of the first graph and the second graph.

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