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公开(公告)号：US11537719B2
公开(公告)日：2022-12-27
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: G08B23/00 , G06F12/16 , G06F12/14 , G06F11/00 , G06F21/57 , G06N3/04 , G06F17/16 , G06F16/901 , G06K9/62
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：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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74.发明申请公开(公告)号：US20210271968A1
公开(公告)日：2021-09-02
申请号：US16967597
申请日：2019-02-11
Applicant: DeepMind Technologies Limited
Inventor： Iaroslav Ganin , Tejas Dattatraya Kulkarni , Oriol Vinyals , Seyed Mohammadali Eslami
Abstract: A generative adversarial neural network system to provide a sequence of actions for performing a task. The system comprises a reinforcement learning neural network subsystem coupled to a simulator and a discriminator neural network. The reinforcement learning neural network subsystem includes a policy recurrent neural network to, at each of a sequence of time steps, select one or more actions to be performed according to an action selection policy, each action comprising one or more control commands for a simulator. The simulator is configured to implement the control commands for the time steps to generate a simulator output. The discriminator neural network is configured to discriminate between the simulator output and training data, to provide a reward signal for the reinforcement learning. The simulator may be non-differentiable simulator, for example a computer program to produce an image or audio waveform or a program to control a robot or vehicle.
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公开(公告)号：US20210073594A1
公开(公告)日：2021-03-11
申请号：US17019919
申请日：2020-09-14
Applicant: DeepMind Technologies Limited
Inventor： Daniel Pieter Wierstra , Yujia Li , Razvan Pascanu , Peter William Battaglia , Theophane Guillaume Weber , Lars Buesing , David Paul Reichert , Arthur Clement Guez , Danilo Jimenez Rezende , Adrià Puigdomènech Badia , Oriol Vinyals , Nicolas Manfred Otto Heess , Sebastien Henri Andre Racaniere
Abstract: A neural network system is proposed. The neural network can be trained by model-based reinforcement learning 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 comprise at least one imagination core which receives a current observation characterizing a current state of the environment, and optionally historical observations, and which includes a model of the environment. The imagination core may be configured to output trajectory data in response to the current observation, and/or historical observations. The trajectory data comprising a sequence of future features of the environment imagined by the imagination core. The system may also include a rollout encoder to encode the features, and an output stage to receive data derived from the rollout embedding and to output action policy data for identifying an action based on the current observation.
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Patent Agency Ranking
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：US10860895B2
公开(公告)日：2020-12-08
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：US20200327359A1
公开(公告)日：2020-10-15
申请号：US16303510
申请日：2017-05-19
Applicant: DEEPMIND TECHNOLOGIES LIMITED
Inventor： Charles Blundell , Oriol Vinyals
Abstract: Methods, systems, and apparatus for classifying a new example using a comparison set of comparison examples. One method includes maintaining a comparison set, the comparison set including comparison examples and a respective label vector for each of the comparison examples, each label vector including a respective score for each label in a predetermined set of labels; receiving a new example; determining a respective attention weight for each comparison example by applying a neural network attention mechanism to the new example and to the comparison examples; and generating a respective label score for each label in the predetermined set of labels from, for each of the comparison examples, the respective attention weight for the comparison example and the respective label vector for the comparison example, in which the respective label score for each of the labels represents a likelihood that the label is a correct label for the new example.
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公开(公告)号：US10776670B2
公开(公告)日：2020-09-15
申请号：US16689058
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 , Arthur Clement Guez , Danilo Jimenez Rezende , Adrià Puigdomènech Badia , Oriol Vinyals , Nicolas Manfred Otto Heess , Sebastien Henri Andre Racaniere
Abstract: A neural network system is proposed. The neural network can be trained by model-based reinforcement learning 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 comprise at least one imagination core which receives a current observation characterizing a current state of the environment, and optionally historical observations, and which includes a model of the environment. The imagination core may be configured to output trajectory data in response to the current observation, and/or historical observations. The trajectory data comprising a sequence of future features of the environment imagined by the imagination core. The system may also include a rollout encoder to encode the features, and an output stage to receive data derived from the rollout embedding and to output action policy data for identifying an action based on the current observation.
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80.发明申请公开(公告)号：US20200285940A1
公开(公告)日：2020-09-10
申请号：US16759561
申请日：2018-10-29
Applicant: DeepMind Technologies Limited
Inventor： Pablo Sprechmann , Siddhant Jayakumar , Jack William Rae , Alexander Pritzel , Adrià Puigdomènech Badia , Oriol Vinyals , Razvan Pascanu , Charles Blundell
Abstract: There is described herein a computer-implemented method of processing an input data item. The method comprises processing the input data item using a parametric model to generate output data, wherein the parametric model comprises a first sub-model and a second sub-model. The processing comprises processing, by the first sub-model, the input data to generate a query data item, retrieving, from a memory storing data point-value pairs, at least one data point-value pair based upon the query data item and modifying weights of the second sub-model based upon the retrieved at least one data point-value pair. The output data is then generated based upon the modified second sub-model.
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