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    GENERATING NEURAL NETWORK OUTPUTS BY ENRICHING LATENT EMBEDDINGS USING SELF-ATTENTION AND CROSS-ATTENTION OPERATIONS
    2.
    发明公开
    GENERATING NEURAL NETWORK OUTPUTS BY ENRICHING LATENT EMBEDDINGS USING SELF-ATTENTION AND CROSS-ATTENTION OPERATIONS 审中-公开

    公开(公告)号：US20230145129A1

    公开(公告)日：2023-05-11

    申请号：US18095925

    申请日：2023-01-11

    Applicant: DeepMind Technologies Limited

    Inventor： Andrew Coulter Jaegle ,  Joao Carreira

    IPC: G06N3/092

    CPC classification number: G06N3/092

    Abstract: This specification describes a method for using a neural network to generate a network output that characterizes an entity. The method includes: obtaining a representation of the entity as a set of data element embeddings, obtaining a set of latent embeddings, and processing: (i) the set of data element embeddings, and (ii) the set of latent embeddings, using the neural network to generate the network output characterizing the entity. The neural network includes: (i) one or more cross-attention blocks, (ii) one or more self-attention blocks, and (iii) an output block. Each cross-attention block updates each latent embedding using attention over some or all of the data element embeddings. Each self-attention block updates each latent embedding using attention over the set of latent embeddings. The output block processes one or more latent embeddings to generate the network output that characterizes the entity.

    IMITATION LEARNING BASED ON PREDICTION OF OUTCOMES
    4.
    发明公开
    IMITATION LEARNING BASED ON PREDICTION OF OUTCOMES 审中-公开

    公开(公告)号：US20240185082A1

    公开(公告)日：2024-06-06

    申请号：US18275722

    申请日：2022-02-04

    Applicant: DeepMind Technologies Limited

    Inventor： Andrew Coulter Jaegle ,  Yury Sulsky ,  Gregory Duncan Wayne ,  Robert David Fergus

    IPC: G06N3/092

    CPC classification number: G06N3/092

    Abstract: A method is proposed of training a policy model to generate action data for controlling an agent to perform a task in an environment. The method comprises: obtaining, for each of a plurality of performances of the task, a corresponding demonstrator trajectory comprising a plurality of sets of state data characterizing the environment at each of a plurality of corresponding successive time steps during the performance of the task; using the demonstrator trajectories to generate a demonstrator model, the demonstrator model being operative to generate, for any said demonstrator trajectory, a value indicative of the probability of the demonstrator trajectory occurring; and jointly training an imitator model and a policy model. The joint training is performed by: generating a plurality of imitation trajectories, each imitation trajectory being generated by repeatedly receiving state data indicating a state of the environment, using the policy model to generate action data indicative of an action, and causing the action to be performed by the agent; training the imitator model using the imitation trajectories, the imitator model being operative to generate, for any said imitation trajectory, a value indicative of the probability of the imitation trajectory occurring; and training the policy model using a reward function which is a measure of the similarity of the demonstrator model and the imitator model.

    GENERATING NEURAL NETWORK OUTPUTS BY ENRICHING LATENT EMBEDDINGS USING SELF-ATTENTION AND CROSS-ATTENTION OPERATIONS
    5.
    发明公开
    GENERATING NEURAL NETWORK OUTPUTS BY ENRICHING LATENT EMBEDDINGS USING SELF-ATTENTION AND CROSS-ATTENTION OPERATIONS 审中-公开

    公开(公告)号：US20240104355A1

    公开(公告)日：2024-03-28

    申请号：US18271611

    申请日：2022-02-03

    Applicant: DeepMind Technologies Limited

    Inventor： Andrew Coulter Jaegle ,  Joao Carreira

    IPC: G06N3/0475 ,  G06N3/084

    CPC classification number: G06N3/0475 ,  G06N3/084

    Abstract: This specification describes a method for using a neural network to generate a network output that characterizes an entity. The method includes: obtaining a representation of the entity as a set of data element embeddings, obtaining a set of latent embeddings, and processing: (i) the set of data element embeddings, and (ii) the set of latent embeddings, using the neural network to generate the network output characterizing the entity. The neural network includes: (i) one or more cross-attention blocks, (ii) one or more self-attention blocks, and (iii) an output block. Each cross-attention block updates each latent embedding using attention over some or all of the data element embeddings. Each self-attention block updates each latent embedding using attention over the set of latent embeddings. The output block processes one or more latent embeddings to generate the network output that characterizes the entity.

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