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公开(公告)号：US20230244452A1
公开(公告)日：2023-08-03
申请号：US18105211
申请日：2023-02-02
Applicant: DeepMind Technologies Limited
Inventor： Yujia Li , David Hugo Choi , Junyoung Chung , Nathaniel Arthur Kushman , Julian Schrittwieser , Rémi Leblond , Thomas Edward Eccles , James Thomas Keeling , Felix Axel Gimeno Gil , Agustín Matías Dal Lago , Thomas Keisuke Hubert , Peter Choy , Cyprien de Masson d'Autume , Esme Sutherland Robson , Oriol Vinyals
IPC: G06F8/30
CPC classification number: G06F8/30
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating computer code using neural networks. One of the methods includes receiving description data describing a computer programming task; receiving a first set of inputs for the computer programming task; generating a plurality of candidate computer programs by sampling a plurality of output sequences from a set of one or more generative neural networks; for each candidate computer program in a subset of the candidate computer programs and for each input in the first set: executing the candidate computer program on the input to generate an output; and selecting, from the candidate computer programs, one or more computer programs as synthesized computer programs for performing the computer programming task based at least in part on the outputs generated by executing the candidate computer programs in the subset on the inputs in the first set of inputs.
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公开(公告)号：US11704541B2
公开(公告)日：2023-07-18
申请号：US16759525
申请日：2018-10-29
Applicant: DeepMind Technologies Limited
Inventor： Yujia Li , Christopher James Dyer , Oriol Vinyals
CPC classification number: G06N3/047 , G06F16/9024 , G06F17/18 , G06N3/045 , G06N3/08
Abstract: There is described a neural network system for generating a graph, the graph comprising a set of nodes and edges. The system comprises one or more neural networks configured to represent a probability distribution over sequences of node generating decisions and/or edge generating decisions, and one or more computers configured to sample the probability distribution represented by the one or more neural networks to generate a graph.
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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：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：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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Patent Agency Ranking
公开(公告)号：US11983269B2
公开(公告)日：2024-05-14
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 , G06F16/901 , G06F17/16 , G06F18/22 , G06F21/57 , G06N3/04 , G06V10/426 , G06V10/82 , G06V30/196
CPC classification number: G06F21/563 , G06F16/9024 , G06F17/16 , G06F18/22 , G06F21/577 , G06N3/04 , G06V10/426 , G06V10/82 , G06V30/1988
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：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：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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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：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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