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    GENERATING AUDIO USING NEURAL NETWORKS
    31.
    发明申请
    GENERATING AUDIO USING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20190251987A1

    公开(公告)日：2019-08-15

    申请号：US16390549

    申请日：2019-04-22

    Applicant: DeepMind Technologies Limited

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

    IPC: G10L25/30 ,  G10L13/06 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an output sequence of audio data that comprises a respective audio sample at each of a plurality of time steps. One of the methods includes, for each of the time steps: providing a current sequence of audio data as input to a convolutional subnetwork, wherein the current sequence comprises the respective audio sample at each time step that precedes the time step in the output sequence, and wherein the convolutional subnetwork is configured to process the current sequence of audio data to generate an alternative representation for the time step; and providing the alternative representation for the time step as input to an output layer, wherein the output layer is configured to: process the alternative representation to generate an output that defines a score distribution over a plurality of possible audio samples for the time step.

    PROCESSING TEXT SEQUENCES USING NEURAL NETWORKS
    32.
    发明申请
    PROCESSING TEXT SEQUENCES USING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20180329897A1

    公开(公告)日：2018-11-15

    申请号：US16032971

    申请日：2018-07-11

    Applicant: DeepMind Technologies Limited

    Inventor： Nal Emmerich Kalchbrenner ,  Karen Simonyan ,  Lasse Espeholt

    IPC: G06F17/28 ,  G06N3/04 ,  G06N3/08 ,  G10L15/197

    CPC classification number: G06F17/289 ,  G06F17/2818 ,  G06N3/0454 ,  G06N3/0472 ,  G06N3/084 ,  G10L15/197

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for neural machine translation. In one aspect, a system is configured to receive an input sequence of source embeddings representing a source sequence of words in a source natural language and to generate an output sequence of target embeddings representing a target sequence of words that is a translation of the source sequence into a target natural language, the system comprising: a dilated convolutional neural network configured to process the input sequence of source embeddings to generate an encoded representation of the source sequence, and a masked dilated convolutional neural network configured to process the encoded representation of the source sequence to generate the output sequence of target embeddings.

    GENERATING OUTPUT EXAMPLES USING RECURRENT NEURAL NETWORKS CONDITIONED ON BIT VALUES
    33.
    发明申请
    GENERATING OUTPUT EXAMPLES USING RECURRENT NEURAL NETWORKS CONDITIONED ON BIT VALUES 有权

    公开(公告)号：US20250117652A1

    公开(公告)日：2025-04-10

    申请号：US18912978

    申请日：2024-10-11

    Applicant: DeepMind Technologies Limited

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

    IPC: G06N3/08 ,  G06F18/2113 ,  G06N3/045 ,  G06N3/063 ,  H03K19/173

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output examples using neural networks. Each output example includes multiple N-bit output values. To generate a given N-bit output value, a first recurrent input comprising the preceding N-bit output value is processed using a recurrent neural network and in accordance with a hidden state to generate a first score distribution. Then, values for the first half of the N bits are selected. A second recurrent input comprising (i) the preceding N-bit output value and (ii) the values for the first half of the N bits are processed using the recurrent neural network and in accordance with the same hidden state to generate a second score distribution. The values for the second half of the N bits of the output value are then selected using the second score distribution.

    Processing text sequences using neural networks
    39.
    发明授权
    Processing text sequences using neural networks 有权

    公开(公告)号：US11321542B2

    公开(公告)日：2022-05-03

    申请号：US16927267

    申请日：2020-07-13

    Applicant: DeepMind Technologies Limited

    Inventor： Nal Emmerich Kalchbrenner ,  Karen Simonyan ,  Lasse Espeholt

    IPC: G06F40/44 ,  G06F40/58 ,  G06N3/04 ,  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.

    CONTIGUOUS SPARSITY PATTERN NEURAL NETWORKS
    40.
    发明申请
    CONTIGUOUS SPARSITY PATTERN NEURAL NETWORKS 有权

    公开(公告)号：US20210012197A1

    公开(公告)日：2021-01-14

    申请号：US16955420

    申请日：2019-02-11

    Applicant: DeepMind Technologies Limited

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

    IPC: G06N3/08 ,  G06F7/523

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using neural networks having Receive an input vector contiguous sparsity patterns. One of the methods includes storing a first parameter matrix of a neural network having a contiguous sparsity pattern in storage associated with a computing device. The computing device performs an inference pass of the neural network to generate an output vector, including reading, from the storage associated with the computing device, one or more activation values from the input vector, reading, from the storage associated with the computing device, a block of non-zero parameter values, and multiplying each of the one or more activation values by one or more of the block of non-zero parameter values.

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