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    SELECTING POINTS IN CONTINUOUS SPACES USING NEURAL NETWORKS
    3.
    发明申请
    SELECTING POINTS IN CONTINUOUS SPACES USING NEURAL NETWORKS 有权

    公开(公告)号：US20220374683A1

    公开(公告)日：2022-11-24

    申请号：US17668050

    申请日：2022-02-09

    Applicant: DeepMind Technologies Limited

    Inventor： Thomas Edward Eccles ,  Ian Michael Gemp ,  János Kramár ,  Marta Garnelo Abellanas ,  Dan Rosenbaum ,  Yoram Bachrach ,  Thore Kurt Hartwig Graepel

    IPC: G06N3/04 ,  G06K9/62

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting an optimal feature point in a continuous domain for a group of agents. A computer-implemented system obtains, for each of a plurality of agents, respective training data that comprises a respective utility score for each of a plurality of discrete points in the continuous domain. The system trains, for each of the plurality of agents and on the respective training data for the agents, a respective neural network that is configured to receive an input comprising a point in the continuous domain and to generate as output a predicted utility score for the agent at the point. And the system identifies the optimal point by optimizing an approximation of the shared outcome function that is defined by, for any given point in the continuous domain, a combination of the predicted utility scores generated by the respective neural networks for each of the plurality of agents by processing an input comprising the given point.

    Neural network architecture for efficient resource allocation
    4.
    发明授权
    Neural network architecture for efficient resource allocation 有权

    公开(公告)号：US11250475B2

    公开(公告)日：2022-02-15

    申请号：US16918805

    申请日：2020-07-01

    Applicant: DeepMind Technologies Limited

    Inventor： Andrea Tacchetti ,  Daniel Joseph Strouse ,  Marta Garnelo Abellanas ,  Thore Kurt Hartwig Graepel ,  Yoram Bachrach

    IPC: G06Q30/02 ,  G06N3/04 ,  G06N3/02 ,  G10L25/30 ,  G06K9/28

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for efficiently allocating resources among participants. Methods can include receiving valuation data specifying, for each of a plurality of entities, a respective valuation for each of a plurality of resource subsets, each resource subset comprising a different combination of one or more resources of a plurality of resources. After receiving valuation data, assigning each resource in the plurality of resources to a respective entity of the plurality of entities based on the valuations and generating, for each particular entity, a respective input representation that is derived from valuations of every other entity in the plurality of entities other than the particular entity. The input representation for each particular entity is processed using a neural network to generate a rule for the particular entity and a payment based on the rule output for the entities.

    NEURAL NETWORK ARCHITECTURE FOR EFFICIENT RESOURCE ALLOCATION
    5.
    发明申请
    NEURAL NETWORK ARCHITECTURE FOR EFFICIENT RESOURCE ALLOCATION 有权

    公开(公告)号：US20220005079A1

    公开(公告)日：2022-01-06

    申请号：US16918805

    申请日：2020-07-01

    Applicant: DeepMind Technologies Limited

    Inventor： Andrea Tacchetti ,  Daniel Joseph Strouse ,  Marta Garnelo Abellanas ,  Thore Kurt Hartwig Graepel ,  Yoram Bachrach

    IPC: G06Q30/02 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for efficiently allocating resources among participants. Methods can include receiving valuation data specifying, for each of a plurality of entities, a respective valuation for each of a plurality of resource subsets, each resource subset comprising a different combination of one or more resources of a plurality of resources. After receiving valuation data, assigning each resource in the plurality of resources to a respective entity of the plurality of entities based on the valuations and generating, for each particular entity, a respective input representation that is derived from valuations of every other entity in the plurality of entities other than the particular entity. The input representation for each particular entity is processed using a neural network to generate a rule for the particular entity and a payment based on the rule output for the entities.

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