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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
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.
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42.发明申请公开(公告)号：US20240378439A1
公开(公告)日：2024-11-14
申请号：US18642641
申请日：2024-04-22
Applicant: DeepMind Technologies Limited
Inventor： Aaron Gerard Antonius van den Oord , Yutian Chen , Danilo Jimenez Rezende , Oriol Vinyals , Joao Ferdinando Gomes de Freitas , Scott Ellison Reed
Abstract: A system comprising a causal convolutional neural network to autoregressively generate a succession of values of a data item conditioned upon previously generated values of the data item. The system includes support memory for a set of support data patches each of which comprises an encoding of an example data item. A soft attention mechanism attends to one or more patches when generating the current item value. The soft attention mechanism determines a set of scores for the support data patches, for example in the form of a soft attention query vector dependent upon the previously generated values of the data item. The soft attention query vector is used to query the memory. When generating the value of the data item at a current iteration layers of the causal convolutional neural network are conditioned upon the support data patches weighted by the scores.
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公开(公告)号：US20240135955A1
公开(公告)日：2024-04-25
申请号：US18519986
申请日：2023-11-27
Applicant: DeepMind Technologies Limited
Inventor： Aaron Gerard Antonius van den Oord , Sander Etienne Lea Dieleman , Nal Emmerich Kalchbrenner , Karen Simonyan , Oriol Vinyals
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.
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44.发明授权公开(公告)号：US11966839B2
公开(公告)日：2024-04-23
申请号：US16758461
申请日：2018-10-25
Applicant: DeepMind Technologies Limited
Inventor： Aaron Gerard Antonius van den Oord , Yutian Chen , Danilo Jimenez Rezende , Oriol Vinyals , Joao Ferdinando Gomes de Freitas , Scott Ellison Reed
Abstract: A system comprising a causal convolutional neural network to autoregressively generate a succession of values of a data item conditioned upon previously generated values of the data item. The system includes support memory for a set of support data patches each of which comprises an encoding of an example data item. A soft attention mechanism attends to one or more patches when generating the current item value. The soft attention mechanism determines a set of scores for the support data patches, for example in the form of a soft attention query vector dependent upon the previously generated values of the data item. The soft attention query vector is used to query the memory. When generating the value of the data item at a current iteration layers of the causal convolutional neural network are conditioned upon the support data patches weighted by the scores.
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公开(公告)号：US11869530B2
公开(公告)日：2024-01-09
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
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.
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Patent Agency Ranking
公开(公告)号：US11568207B2
公开(公告)日：2023-01-31
申请号：US16586323
申请日：2019-09-27
Applicant: DeepMind Technologies Limited
Inventor： Aaron Gerard Antonius van den Oord , Yazhe Li , Oriol Vinyals
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an encoder neural network that is configured to process an input observation to generate a latent representation of the input observation. In one aspect, a method includes: obtaining a sequence of observations; for each observation in the sequence of observations, processing the observation using the encoder neural network to generate a latent representation of the observation; for each of one or more given observations in the sequence of observations: generating a context latent representation of the given observation; and generating, from the context latent representation of the given observation, a respective estimate of the latent representations of one or more particular observations that are after the given observation in the sequence of observations.
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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
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.
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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
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.
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公开(公告)号：US20210020160A1
公开(公告)日：2021-01-21
申请号：US17061437
申请日：2020-10-01
Applicant: DeepMind Technologies Limited
Inventor： Yutian Chen , Scott Ellison Reed , Aaron Gerard Antonius van den Oord , Oriol Vinyals , Heiga Zen , Ioannis Alexandros Assael , Brendan Shillingford , Joao Ferdinando Gomes de Freitas
IPC: G10L13/047 , G10L13/033 , G06N3/08 , G10L13/00
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an adaptive audio-generation model. One of the methods includes generating an adaptive audio-generation model including learning a plurality of embedding vectors and parameter values of a neural network using training data comprising first text and audio data representing a plurality of different individual speakers speaking portions of the first text, wherein the plurality of embedding vectors represent respective voice characteristics of the plurality of different individual speakers. The adaptive audio-generation model is adapted for a new individual speaker using adaptation data comprising second text and audio data representing the new individual speaker speaking portions of the second text, the new individual speaker being different from each of the plurality of individual speakers, wherein adapting the audio-generation model includes learning a new embedding vector for the new individual speaker.
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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
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.
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