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    Energy-based associative memory neural networks
    1.
    发明授权
    Energy-based associative memory neural networks 有权

    公开(公告)号：US12277487B2

    公开(公告)日：2025-04-15

    申请号：US17441463

    申请日：2020-05-19

    Applicant: DeepMind Technologies Limited

    Inventor： Sergey Bartunov ,  Jack William Rae ,  Timothy Paul Lillicrap ,  Simon Osindero

    IPC: G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing associative memory. In one aspect a system comprises an associative memory neural network to process an input to generate an output that defines an energy corresponding to the input. A reading subsystem retrieves stored information from the associative memory neural network. The reading subsystem performs operations including receiving a given, i.e. query, input and retrieving a data element from the associative memory neural network that is associated with the given input. The retrieving is performed by iteratively adjusting the given input using the associative memory neural network.

    GATED ATTENTION NEURAL NETWORKS
    2.
    发明申请
    GATED ATTENTION NEURAL NETWORKS 有权

    公开(公告)号：US20220366218A1

    公开(公告)日：2022-11-17

    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: G06N3/04 ,  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.

    AUGMENTING ATTENTION-BASED NEURAL NETWORKS TO SELECTIVELY ATTEND TO PAST INPUTS
    5.
    发明公开
    AUGMENTING ATTENTION-BASED NEURAL NETWORKS TO SELECTIVELY ATTEND TO PAST INPUTS 审中-公开

    公开(公告)号：US20240046103A1

    公开(公告)日：2024-02-08

    申请号：US18486060

    申请日：2023-10-12

    Applicant: DeepMind Technologies Limited

    Inventor： Jack William Rae ,  Anna Potapenko ,  Timothy Paul Lillicrap

    IPC: G06N3/084 ,  G06N3/08 ,  G06F18/214 ,  G06N3/047

    CPC classification number: G06N3/084 ,  G06N3/08 ,  G06F18/2148 ,  G06N3/047

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input that is a sequence to generate a network output. In one aspect, one of the methods includes, for each particular sequence of layer inputs: for each attention layer in the neural network: maintaining episodic memory data; maintaining compressed memory data; receiving a layer input to be processed by the attention layer; and applying an attention mechanism over (i) the compressed representation in the compressed memory data for the layer, (ii) the hidden states in the episodic memory data for the layer, and (iii) the respective hidden state at each of the plurality of input positions in the particular network input to generate a respective activation for each input position in the layer input.

    Augmenting attentioned-based neural networks to selectively attend to past inputs
    6.
    发明授权
    Augmenting attentioned-based neural networks to selectively attend to past inputs 有权

    公开(公告)号：US11829884B2

    公开(公告)日：2023-11-28

    申请号：US17033396

    申请日：2020-09-25

    Applicant: DeepMind Technologies Limited

    Inventor： Jack William Rae ,  Anna Potapenko ,  Timothy Paul Lillicrap

    IPC: G06N3/08 ,  G06N3/084 ,  G06F18/214 ,  G06N3/047

    CPC classification number: G06N3/084 ,  G06F18/2148 ,  G06N3/047 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input that is a sequence to generate a network output. In one aspect, one of the methods includes, for each particular sequence of layer inputs: for each attention layer in the neural network: maintaining episodic memory data; maintaining compressed memory data; receiving a layer input to be processed by the attention layer; and applying an attention mechanism over (i) the compressed representation in the compressed memory data for the layer, (ii) the hidden states in the episodic memory data for the layer, and (iii) the respective hidden state at each of the plurality of input positions in the particular network input to generate a respective activation for each input position in the layer input.

    AUGMENTING ATTENTIONED-BASED NEURAL NETWORKS TO SELECTIVELY ATTEND TO PAST INPUTS
    7.
    发明申请
    AUGMENTING ATTENTIONED-BASED NEURAL NETWORKS TO SELECTIVELY ATTEND TO PAST INPUTS 有权

    公开(公告)号：US20210089829A1

    公开(公告)日：2021-03-25

    申请号：US17033396

    申请日：2020-09-25

    Applicant: DeepMind Technologies Limited

    Inventor： Jack William Rae ,  Anna Potapenko ,  Timothy Paul Lillicrap

    IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input that is a sequence to generate a network output. In one aspect, one of the methods includes, for each particular sequence of layer inputs: for each attention layer in the neural network: maintaining episodic memory data; maintaining compressed memory data; receiving a layer input to be processed by the attention layer; and applying an attention mechanism over (i) the compressed representation in the compressed memory data for the layer, (ii) the hidden states in the episodic memory data for the layer, and (iii) the respective hidden state at each of the plurality of input positions in the particular network input to generate a respective activation for each input position in the layer input.

    GATED ATTENTION NEURAL NETWORKS
    8.
    发明公开
    GATED ATTENTION NEURAL NETWORKS 审中-公开

    公开(公告)号：US20240320469A1

    公开(公告)日：2024-09-26

    申请号：US18679200

    申请日：2024-05-30

    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.

    ENERGY-BASED ASSOCIATIVE MEMORY NEURAL NETWORKS
    9.
    发明申请
    ENERGY-BASED ASSOCIATIVE MEMORY NEURAL NETWORKS 有权

    公开(公告)号：US20220180147A1

    公开(公告)日：2022-06-09

    申请号：US17441463

    申请日：2020-05-19

    Applicant: DeepMind Technologies Limited

    Inventor： Sergey Bartunov ,  Jack William Rae ,  Timothy Paul Lillicrap ,  Simon Osindero

    IPC: G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing associative memory. In one aspect a system comprises an associative memory neural network to process an input to generate an output that defines an energy corresponding to the input. A reading subsystem retrieves stored information from the associative memory neural network. The reading subsystem performs operations including receiving a given, i.e. query, input and retrieving a data element from the associative memory neural network that is associated with the given input. The retrieving is performed by iteratively adjusting the given input using the associative memory neural network.

    Scalable and compressive neural network data storage system
    10.
    发明授权
    Scalable and compressive neural network data storage system 有权

    公开(公告)号：US11983617B2

    公开(公告)日：2024-05-14

    申请号：US17102318

    申请日：2020-11-23

    Applicant: DeepMind Technologies Limited

    Inventor： Jack William Rae ,  Timothy Paul Lillicrap ,  Sergey Bartunov

    IPC: G06N3/045 ,  G06F16/22 ,  G06N3/08

    CPC classification number: G06N3/045 ,  G06F16/2272 ,  G06N3/08

    Abstract: A system for compressed data storage using a neural network. The system comprises a memory comprising a plurality of memory locations configured to store data; a query neural network configured to process a representation of an input data item to generate a query; an immutable key data store comprising key data for indexing the plurality of memory locations; an addressing system configured to process the key data and the query to generate a weighting associated with the plurality of memory locations; a memory read system configured to generate output memory data from the memory based upon the generated weighting associated with the plurality of memory locations and the data stored at the plurality of memory locations; and a memory write system configured to write received write data to the memory based upon the generated weighting associated with the plurality of memory locations.

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