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公开(公告)号：US20230134742A1
公开(公告)日：2023-05-04
申请号：US18087704
申请日：2022-12-22
Applicant: DeepMind Technologies Limited
Inventor： Yujia Li , Chenjie Gu , Thomas Dullien , Oriol Vinyals , Pushmeet Kohli
IPC: G06F21/56 , G06F21/57 , G06N3/04 , G06F17/16 , G06F16/901 , G06F18/22 , G06V30/196 , G06V10/82 , G06V10/426
Abstract: There is described a neural network system implemented by one or more computers for determining graph similarity. The neural network system comprises one or more neural networks configured to process an input graph to generate a node state representation vector for each node of the input graph and an edge representation vector for each edge of the input graph; and process the node state representation vectors and the edge representation vectors to generate a vector representation of the input graph. The neural network system further comprises one or more processors configured to: receive a first graph; receive a second graph; generate a vector representation of the first graph; generate a vector representation of the second graph; determine a similarity score for the first graph and the second graph based upon the vector representations of the first graph and the second graph.
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公开(公告)号：US11636283B2
公开(公告)日：2023-04-25
申请号：US16889125
申请日：2020-06-01
Applicant: DeepMind Technologies Limited
Inventor： Benjamin Poole , Aaron Gerard Antonius van den Oord , Ali Razavi-Nematollahi , Oriol Vinyals
Abstract: A variational autoencoder (VAE) neural network system, comprising an encoder neural network to encode an input data item to define a posterior distribution for a set of latent variables, and a decoder neural network to generate an output data item representing values of a set of latent variables sampled from the posterior distribution. The system is configured for training with an objective function including a term dependent on a difference between the posterior distribution and a prior distribution. The prior and posterior distributions are arranged so that they cannot be matched to one another. The VAE system may be used for compressing and decompressing data.
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公开(公告)号：US11355097B2
公开(公告)日：2022-06-07
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 , G10L13/00 , G06N3/04 , G06N3/08
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：US20210342670A1
公开(公告)日：2021-11-04
申请号：US17375250
申请日：2021-07-14
Applicant: DeepMind Technologies Limited
Inventor： Aaron Gerard Antonius van den Oord , Sander Etienne Lea Dieleman , Nal Emmerich Kalchbrenner , Karen Simonyan , Oriol Vinyals , Lasse Espeholt
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing sequences using convolutional neural networks. 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：US20200293899A1
公开(公告)日：2020-09-17
申请号：US16759567
申请日：2018-10-26
Applicant: DeepMind Technologies Limited
Inventor： Chrisantha Thomas Fernando , Karen Simonyan , Koray Kavukcuoglu , Hanxiao Liu , Oriol Vinyals
IPC: G06N3/08 , G06N3/04 , G06F16/901
Abstract: A computer-implemented method for automatically determining a neural network architecture represents a neural network architecture as a data structure defining a hierarchical set of directed acyclic graphs in multiple levels. Each graph has an input, an output, and a plurality of nodes between the input and the output. At each level, a corresponding set of the nodes are connected pairwise by directed edges which indicate operations performed on outputs of one node to generate an input to another node. Each level is associated with a corresponding set of operations. At a lowest level, the operations associated with each edge are selected from a set of primitive operations. The method includes repeatedly generating new sample neural network architectures, and evaluating their fitness. The modification is performed by selecting a level, selecting two nodes at that level, and modifying, removing or adding an edge between those nodes according to operations associated with lower levels of the hierarchy.
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Patent Agency Ranking
公开(公告)号：US10671889B2
公开(公告)日：2020-06-02
申请号：US16586014
申请日：2019-09-27
Applicant: DeepMind Technologies Limited
Inventor： Benjamin Poole , Aaron Gerard Antonius van den Oord , Ali Razavi-Nematollahi , Oriol Vinyals
Abstract: A variational autoencoder (VAE) neural network system, comprising an encoder neural network to encode an input data item to define a posterior distribution for a set of latent variables, and a decoder neural network to generate an output data item representing values of a set of latent variables sampled from the posterior distribution. The system is configured for training with an objective function including a term dependent on a difference between the posterior distribution and a prior distribution. The prior and posterior distributions are arranged so that they cannot be matched to one another. The VAE system may be used for compressing and decompressing data.
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公开(公告)号：US20200104640A1
公开(公告)日：2020-04-02
申请号：US16586014
申请日：2019-09-27
Applicant: DeepMind Technologies Limited
Inventor： Benjamin Poole , Aaron Gerard Antonius van den Oord , Ali Razavi-Nematollahi , Oriol Vinyals
Abstract: A variational autoencoder (VAE) neural network system, comprising an encoder neural network to encode an input data item to define a posterior distribution for a set of latent variables, and a decoder neural network to generate an output data item representing values of a set of latent variables sampled from the posterior distribution. The system is configured for training with an objective function including a term dependent on a difference between the posterior distribution and a prior distribution. The prior and posterior distributions are arranged so that they cannot be matched to one another. The VAE system may be used for compressing and decompressing data.
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公开(公告)号：US20200082227A1
公开(公告)日：2020-03-12
申请号：US16689017
申请日：2019-11-19
Applicant: DeepMind Technologies Limited
Inventor： Daniel Pieter Wierstra , Yujia Li , Razvan Pascanu , Peter William Battaglia , Theophane Guillaume Weber , Lars Buesing , David Paul Reichert , Oriol Vinyals , Nicolas Manfred Otto Heess , Sebastien Henri Andre Racaniere
Abstract: A neural network system is proposed to select actions to be performed by an agent interacting with an environment to perform a task in an attempt to achieve a specified result. The system may include a controller to receive state data and context data, and to output action data. The system may also include an imagination module to receive the state and action data, and to output consequent state data. The system may also include a manager to receive the state data and the context data, and to output route data which defines whether the system is to execute an action or to imagine. The system may also include a memory to store the context data.
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公开(公告)号：US20200005152A1
公开(公告)日：2020-01-02
申请号：US16511496
申请日：2019-07-15
Applicant: DeepMind Technologies Limited
Inventor： Charles Blundell , Meire Fortunato , Oriol Vinyals
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network. In one aspect, a method includes maintaining data specifying, for each of the network parameters, current values of a respective set of distribution parameters that define a posterior distribution over possible values for the network parameter. A respective current training value for each of the network parameters is determined from a respective temporary gradient value for the network parameter. The current values of the respective sets of distribution parameters for the network parameters are updated in accordance with the respective current training values for the network parameters. The trained values of the network parameters are determined based on the updated current values of the respective sets of distribution parameters.
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公开(公告)号：US20190354689A1
公开(公告)日：2019-11-21
申请号：US16416070
申请日：2019-05-17
Applicant: DeepMind Technologies Limited
Inventor： Yujia Li , Chenjie Gu , Thomas Dullien , Oriol Vinyals , Pushmeet Kohli
IPC: G06F21/57 , G06N3/04 , G06K9/62 , G06F16/901 , G06F17/16
Abstract: There is described a neural network system implemented by one or more computers for determining graph similarity. The neural network system comprises one or more neural networks configured to process an input graph to generate a node state representation vector for each node of the input graph and an edge representation vector for each edge of the input graph; and process the node state representation vectors and the edge representation vectors to generate a vector representation of the input graph. The neural network system further comprises one or more processors configured to: receive a first graph; receive a second graph; generate a vector representation of the first graph; generate a vector representation of the second graph; determine a similarity score for the first graph and the second graph based upon the vector representations of the first graph and the second graph.
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