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    GENERATING PREDICTION OUTPUTS USING DYNAMIC GRAPHS
    1.
    发明申请
    GENERATING PREDICTION OUTPUTS USING DYNAMIC GRAPHS 有权

    公开(公告)号：US20210383228A1

    公开(公告)日：2021-12-09

    申请号：US17338974

    申请日：2021-06-04

    Applicant: DeepMind Technologies Limited

    Inventor： Petar Velickovic ,  Charles Blundell ,  Oriol Vinyals ,  Razvan Pascanu ,  Lars Buesing ,  Matthew Overlan

    IPC: G06N3/08 ,  G06N3/04 ,  G06F16/23 ,  G06F16/901

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating prediction outputs characterizing a set of entities. In one aspect, a method comprises: obtaining data defining a graph, comprising: (i) a set of nodes, wherein each node represents a respective entity from the set of entities, (ii) a current set of edges, wherein each edge connects a pair of nodes, and (iii) a respective current embedding of each node; at each of a plurality of time steps: updating the respective current embedding of each node, comprising processing data defining the graph using a graph neural network; and updating the current set of edges based at least in part on the updated embeddings of the nodes; and at one or more of the plurality of time steps: generating a prediction output characterizing the set of entities based on the current embeddings of the nodes.

    Gated attention neural networks
    7.
    发明授权
    Gated attention neural networks 有权

    公开(公告)号：US12033055B2

    公开(公告)日：2024-07-09

    申请号：US17763984

    申请日：2020-09-07

    Applicant: DeepMind Technologies Limited

    Inventor： Emilio Parisotto ,  Hasuk Song ,  Jack William Rae ,  Siddhant Madhu Jayakumar ,  Maxwell Elliot Jaderberg ,  Razvan Pascanu ,  Caglar Gulcehre

    IPC: G06N3/044 ,  G06N3/048 ,  G06N3/08

    CPC classification number: G06N3/044 ,  G06N3/048 ,  G06N3/08

    Abstract: A system including an attention neural network that is configured to receive an input sequence and to process the input sequence to generate an output is described. The attention neural network includes: an attention block configured to receive a query input, a key input, and a value input that are derived from an attention block input. The attention block includes an attention neural network layer configured to: receive an attention layer input derived from the query input, the key input, and the value input, and apply an attention mechanism to the query input, the key input, and the value input to generate an attention layer output for the attention neural network layer; and a gating neural network layer configured to apply a gating mechanism to the attention block input and the attention layer output of the attention neural network layer to generate a gated attention output.

    PROGRESSIVE NEURAL NETWORKS
    9.
    发明申请
    PROGRESSIVE NEURAL NETWORKS 有权

    公开(公告)号：US20210201116A1

    公开(公告)日：2021-07-01

    申请号：US17201542

    申请日：2021-03-15

    Applicant: DeepMind Technologies Limited

    Inventor： Neil Charles Rabinowitz ,  Guillaume Desjardins ,  Andrei-Alexandru Rusu ,  Koray Kavukcuoglu ,  Raia Thais Hadsell ,  Razvan Pascanu ,  James Kirkpatrick ,  Hubert Josef Soyer

    IPC: G06N3/04 ,  G06F17/16 ,  G06N3/08

    Abstract: Methods and systems for performing a sequence of machine learning tasks. One system includes a sequence of deep neural networks (DNNs), including: a first DNN corresponding to a first machine learning task, wherein the first DNN comprises a first plurality of indexed layers, and each layer in the first plurality of indexed layers is configured to receive a respective layer input and process the layer input to generate a respective layer output; and one or more subsequent DNNs corresponding to one or more respective machine learning tasks, wherein each subsequent DNN comprises a respective plurality of indexed layers, and each layer in a respective plurality of indexed layers with index greater than one receives input from a preceding layer of the respective subsequent DNN, and one or more preceding layers of respective preceding DNNs, wherein a preceding layer is a layer whose index is one less than the current index.

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