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    Generating output examples using bit blocks
    2.
    发明授权
    Generating output examples using bit blocks 有权

    公开(公告)号：US11853861B2

    公开(公告)日：2023-12-26

    申请号：US17962881

    申请日：2022-10-10

    Applicant: DeepMind Technologies Limited

    Inventor： Nal Emmerich Kalchbrenner ,  Karen Simonyan ,  Erich Konrad Elsen

    IPC: G06N3/04 ,  G06N3/047 ,  G06N3/088 ,  G06N3/045

    CPC classification number: G06N3/047 ,  G06N3/045 ,  G06N3/088

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output examples using neural networks. One of the methods includes receiving a request to generate an output example of a particular type, accessing dependency data, and generating the output example by, at each of a plurality of generation time steps: identifying one or more current blocks for the generation time step, wherein each current block is a block for which the values of the bits in all of the other blocks identified in the dependency for the block have already been generated; and generating the values of the bits in the current blocks for the generation time step conditioned on, for each current block, the already generated values of the bits in the other blocks identified in the dependency for the current block.

    PROCESSING TEXT SEQUENCES USING NEURAL NETWORKS
    6.
    发明申请
    PROCESSING TEXT SEQUENCES USING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200342183A1

    公开(公告)日：2020-10-29

    申请号：US16927267

    申请日：2020-07-13

    Applicant: DeepMind Technologies Limited

    Inventor： Nal Emmerich Kalchbrenner ,  Karen Simonyan ,  Lasse Espeholt

    IPC: G06F40/58 ,  G06N3/04 ,  G06F40/44 ,  G06N3/08 ,  G10L15/197

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for language modeling. In one aspect, a system comprises: a masked convolutional decoder neural network that comprises a plurality of masked convolutional neural network layers and is configured to generate a respective probability distribution over a set of possible target embeddings at each of a plurality of time steps; and a modeling engine that is configured to use the respective probability distribution generated by the decoder neural network at each of the plurality of time steps to estimate a probability that a string represented by the target embeddings corresponding to the plurality of time steps belongs to the natural language.

    Speech recognition using convolutional neural networks
    8.
    发明授权
    Speech recognition using convolutional neural networks 有权

    公开(公告)号：US11069345B2

    公开(公告)日：2021-07-20

    申请号：US16719424

    申请日：2019-12-18

    Applicant: DeepMind Technologies Limited

    Inventor： Aaron Gerard Antonius van den Oord ,  Sander Etienne Lea Dieleman ,  Nal Emmerich Kalchbrenner ,  Karen Simonyan ,  Oriol Vinyals ,  Lasse Espeholt

    IPC: G10L15/16 ,  G06N3/04 ,  G06N3/08 ,  G10L15/02 ,  G10L15/22

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing speech recognition by generating a neural network output from an audio data input sequence, where the neural network output characterizes words spoken in the audio data input sequence. One of the methods includes, for each of the audio data inputs, providing a current audio data input sequence that comprises the audio data input and the audio data inputs preceding the audio data input in the audio data input sequence to a convolutional subnetwork comprising a plurality of dilated convolutional neural network layers, wherein the convolutional subnetwork is configured to, for each of the plurality of audio data inputs: receive the current audio data input sequence for the audio data input, and process the current audio data input sequence to generate an alternative representation for the audio data input.

    Associative long short-term memory neural network layers
    9.
    发明授权
    Associative long short-term memory neural network layers 有权

    公开(公告)号：US11010663B2

    公开(公告)日：2021-05-18

    申请号：US15395553

    申请日：2016-12-30

    Applicant: DeepMind Technologies Limited

    Inventor： Ivo Danihelka ,  Nal Emmerich Kalchbrenner ,  Gregory Duncan Wayne ,  Benigno Uría-Martínez ,  Alexander Benjamin Graves

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Systems, methods, and apparatus, including computer programs encoded on a computer storage medium, related to associative long short-term memory (LSTM) neural network layers configured to maintain N copies of an internal state for the associative LSTM layer, N being an integer greater than one. In one aspect, a system includes a recurrent neural network including an associative LSTM layer, wherein the associative LSTM layer is configured to, for each time step, receive a layer input, update each of the N copies of the internal state using the layer input for the time step and a layer output generated by the associative LSTM layer for a preceding time step, and generate a layer output for the time step using the N updated copies of the internal state.

    Speech recognition using convolutional neural networks
    10.
    发明授权
    Speech recognition using convolutional neural networks 有权

    公开(公告)号：US10586531B2

    公开(公告)日：2020-03-10

    申请号：US16209661

    申请日：2018-12-04

    Applicant: DeepMind Technologies Limited

    Inventor： Aaron Gerard Antonius van den Oord ,  Sander Etienne Lea Dieleman ,  Nal Emmerich Kalchbrenner ,  Karen Simonyan ,  Oriol Vinyals ,  Lasse Espeholt

    IPC: G10L15/16 ,  G06N3/04 ,  G10L15/02 ,  G10L15/22 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing speech recognition by generating a neural network output from an audio data input sequence, where the neural network output characterizes words spoken in the audio data input sequence. One of the methods includes, for each of the audio data inputs, providing a current audio data input sequence that comprises the audio data input and the audio data inputs preceding the audio data input in the audio data input sequence to a convolutional subnetwork comprising a plurality of dilated convolutional neural network layers, wherein the convolutional subnetwork is configured to, for each of the plurality of audio data inputs: receive the current audio data input sequence for the audio data input, and process the current audio data input sequence to generate an alternative representation for the audio data input.

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