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    Deep neural network system for similarity-based graph representations
    2.
    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

    申请人： DeepMind Technologies Limited

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

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

    摘要： 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.

    DEEP NEURAL NETWORK SYSTEM FOR SIMILARITY-BASED GRAPH REPRESENTATIONS
    3.
    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

    申请人： DeepMind Technologies Limited

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

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

    摘要： 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.

    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

    申请人： DeepMind Technologies Limited

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

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

    摘要： 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.