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    Generating output examples using bit blocks
    53.
    发明授权
    Generating output examples using bit blocks 有权

    公开(公告)号：US11468295B2

    公开(公告)日：2022-10-11

    申请号：US15985628

    申请日：2018-05-21

    Applicant: DeepMind Technologies Limited

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

    IPC: G06N3/04 ,  G06N3/08

    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.

    GENERATING AUDIO USING NEURAL NETWORKS
    54.
    发明申请
    GENERATING AUDIO USING NEURAL NETWORKS 有权

    公开(公告)号：US20220319533A1

    公开(公告)日：2022-10-06

    申请号：US17838985

    申请日：2022-06-13

    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 ,  G06N3/04 ,  G10L13/06

    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.

    Training action selection neural networks using look-ahead search
    55.
    发明授权
    Training action selection neural networks using look-ahead search 有权

    公开(公告)号：US11449750B2

    公开(公告)日：2022-09-20

    申请号：US16617478

    申请日：2018-05-28

    Applicant: DEEPMIND TECHNOLOGIES LIMITED

    Inventor： Karen Simonyan ,  David Silver ,  Julian Schrittwieser

    IPC: G06N3/08 ,  G06N7/00

    Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage media, for training an action selection neural network. One of the methods includes receiving an observation characterizing a current state of the environment; determining a target network output for the observation by performing a look ahead search of possible future states of the environment starting from the current state until the environment reaches a possible future state that satisfies one or more termination criteria, wherein the look ahead search is guided by the neural network in accordance with current values of the network parameters; selecting an action to be performed by the agent in response to the observation using the target network output generated by performing the look ahead search; and storing, in an exploration history data store, the target network output in association with the observation for use in updating the current values of the network parameters.

    Generative Adversarial Networks with Temporal and Spatial Discriminators for Efficient Video Generation
    56.
    发明申请
    Generative Adversarial Networks with Temporal and Spatial Discriminators for Efficient Video Generation 有权

    公开(公告)号：US20220230276A1

    公开(公告)日：2022-07-21

    申请号：US17613694

    申请日：2020-05-22

    Applicant: DeepMind Technologies Limited

    Inventor： Aidan Clark ,  Jeffrey Donahue ,  Karen Simonyan

    IPC: G06T3/40 ,  G06N3/08 ,  G06N3/04

    Abstract: The present disclosure proposes the use of a dual discriminator network that comprises a temporal discriminator network for discriminating based on temporal features of a series of images and a spatial discriminator network for discriminating based on spatial features of individual images. The training methods described herein provide improvements in computational efficiency. This is achieved by applying the spatial discriminator network to a set of one or more images that have reduced temporal resolution and applying the temporal discriminator network to a set of images that have reduced spatial resolution. This allows each of the discriminator networks to be applied more efficiently in order to produce a discriminator score for use in training the generator, whilst maintaining accuracy of the discriminator network. In addition, this allows a generator network to be trained to more accurately generate sequences of images, through the use of the improved discriminator.

    Generating audio using neural networks
    57.
    发明授权
    Generating audio using neural networks 有权

    公开(公告)号：US11386914B2

    公开(公告)日：2022-07-12

    申请号：US17020348

    申请日：2020-09-14

    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 ,  G06N3/04 ,  G10L13/06

    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.

    GENERATING VIDEO FRAMES USING NEURAL NETWORKS
    59.
    发明申请
    GENERATING VIDEO FRAMES USING NEURAL NETWORKS 有权

    公开(公告)号：US20210019555A1

    公开(公告)日：2021-01-21

    申请号：US16338338

    申请日：2017-09-29

    Applicant: DeepMind Technologies Limited

    Inventor： Nal Emmerich Kalchbrenner ,  Aaron Gerard Antonius van den Oord ,  Karen Simonyan

    IPC: G06K9/62 ,  G06K9/46 ,  G06K9/00 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating video frames using neural networks. One of the methods includes processing a sequence of video frames using an encoder neural network to generate an encoded representation; and generating a predicted next frame pixel by pixel according to a pixel order and a channel order, comprising: for each color channel of each pixel, providing as input to a decoder neural network (i) the encoded representation, (ii) color values for any pixels before the pixel in the pixel order, and (iii) color values for the pixel for any color channels before the color channel in the channel order, wherein the decoder neural network is configured to generate an output defining a score distribution over a plurality of possible color values, and determining the color value for the color channel of the pixel by sampling from the score distribution.

    Whitened neural network layers
    60.
    发明授权
    Whitened neural network layers 有权

    公开(公告)号：US10762421B2

    公开(公告)日：2020-09-01

    申请号：US15174020

    申请日：2016-06-06

    Applicant: DeepMind Technologies Limited

    Inventor： Guillaume Desjardins ,  Karen Simonyan ,  Koray Kavukcuoglu ,  Razvan Pascanu

    IPC: G06N3/08 ,  G06N20/00 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using a neural network system that includes a whitened neural network layer. One of the methods includes receiving an input activation generated by a layer before the whitened neural network layer in the sequence; processing the received activation in accordance with a set of whitening parameters to generate a whitened activation; processing the whitened activation in accordance with a set of layer parameters to generate an output activation; and providing the output activation as input to a neural network layer after the whitened neural network layer in the sequence.

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