公开(公告)号：US12033055B2

公开(公告)日：2024-07-09

申请号：US17763984

申请日：2020-09-07

Applicant: DeepMind Technologies Limited

Inventor： Emilio Parisotto ,  Hasuk Song ,  Jack William Rae ,  Siddhant Madhu Jayakumar ,  Maxwell Elliot Jaderberg ,  Razvan Pascanu ,  Caglar Gulcehre

IPC: G06N3/044 ,  G06N3/048 ,  G06N3/08

CPC classification number: G06N3/044 ,  G06N3/048 ,  G06N3/08

Abstract: A system including an attention neural network that is configured to receive an input sequence and to process the input sequence to generate an output is described. The attention neural network includes: an attention block configured to receive a query input, a key input, and a value input that are derived from an attention block input. The attention block includes an attention neural network layer configured to: receive an attention layer input derived from the query input, the key input, and the value input, and apply an attention mechanism to the query input, the key input, and the value input to generate an attention layer output for the attention neural network layer; and a gating neural network layer configured to apply a gating mechanism to the attention block input and the attention layer output of the attention neural network layer to generate a gated attention output.

公开(公告)号：US11842261B2

公开(公告)日：2023-12-12

申请号：US17121679

申请日：2020-12-14

Applicant: DeepMind Technologies Limited

Inventor： Iain Robert Dunning ,  Wojciech Czarnecki ,  Maxwell Elliot Jaderberg

IPC: G06N3/045 ,  G06N3/08 ,  G06N3/047 ,  G06F17/18

CPC classification number: G06N3/045 ,  G06F17/18 ,  G06N3/047 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for reinforcement learning. One of the methods includes selecting an action to be performed by the agent using both a slow updating recurrent neural network and a fast updating recurrent neural network that receives a fast updating input that includes the hidden state of the slow updating recurrent neural network.

公开(公告)号：US11734572B2

公开(公告)日：2023-08-22

申请号：US16995307

申请日：2020-08-17

Applicant: DeepMind Technologies Limited

Inventor： Maxwell Elliot Jaderberg ,  Karen Simonyan ,  Andrew Zisserman ,  Koray Kavukcuoglu

IPC: G06N3/045 ,  G06N3/088 ,  G06N3/084 ,  G06V10/44

CPC classification number: G06N3/084 ,  G06N3/045 ,  G06N3/088 ,  G06V10/454

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using an image processing neural network system that includes a spatial transformer module. One of the methods includes receiving an input feature map derived from the one or more input images, and applying a spatial transformation to the input feature map to generate a transformed feature map, comprising: processing the input feature map to generate spatial transformation parameters for the spatial transformation, and sampling from the input feature map in accordance with the spatial transformation parameters to generate the transformed feature map.

公开(公告)号：US20210097373A1

公开(公告)日：2021-04-01

申请号：US17121679

申请日：2020-12-14

Applicant: DeepMind Technologies Limited

Inventor： Iain Robert Dunning ,  Wojciech Czarnecki ,  Maxwell Elliot Jaderberg

IPC: G06N3/04 ,  G06F17/18 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for reinforcement learning. One of the methods includes selecting an action to be performed by the agent using both a slow updating recurrent neural network and a fast updating recurrent neural network that receives a fast updating input that includes the hidden state of the slow updating recurrent neural network.

公开(公告)号：US20210034909A1

公开(公告)日：2021-02-04

申请号：US16995307

申请日：2020-08-17

Applicant: DeepMind Technologies Limited

Inventor： Maxwell Elliot Jaderberg ,  Karen Simonyan ,  Andrew Zisserman ,  Koray Kavukcuoglu

IPC: G06K9/52 ,  G06N3/04 ,  G06K9/46 ,  G06N3/08 ,  G06K9/03

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using an image processing neural network system that includes a spatial transformer module. One of the methods includes receiving an input feature map derived from the one or more input images, and applying a spatial transformation to the input feature map to generate a transformed feature map, comprising: processing the input feature map to generate spatial transformation parameters for the spatial transformation, and sampling from the input feature map in accordance with the spatial transformation parameters to generate the transformed feature map.

公开(公告)号：US20190258938A1

公开(公告)日：2019-08-22

申请号：US16403385

申请日：2019-05-03

Applicant: DeepMind Technologies Limited

Inventor： Volodymyr Mnih ,  Wojciech Czarnecki ,  Maxwell Elliot Jaderberg ,  Tom Schaul ,  David Silver ,  Koray Kavukcuoglu

IPC: G06N3/08 ,  G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. The method includes: training an action selection policy neural network, and during the training of the action selection neural network, training one or more auxiliary control neural networks and a reward prediction neural network. Each of the auxiliary control neural networks is configured to receive a respective intermediate output generated by the action selection policy neural network and generate a policy output for a corresponding auxiliary control task. The reward prediction neural network is configured to receive one or more intermediate outputs generated by the action selection policy neural network and generate a corresponding predicted reward. Training each of the auxiliary control neural networks and the reward prediction neural network comprises adjusting values of the respective auxiliary control parameters, reward prediction parameters, and the action selection policy network parameters.

公开(公告)号：US20180330185A1

公开(公告)日：2018-11-15

申请号：US16041567

申请日：2018-07-20

Applicant: DeepMind Technologies Limited

Inventor： Maxwell Elliot Jaderberg ,  Karen Simonyan ,  Andrew Zisserman ,  Koray Kavukcuoglu

IPC: G06K9/52 ,  G06N3/08 ,  G06K9/46 ,  G06K9/03 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using an image processing neural network system that includes a spatial transformer module. One of the methods includes receiving an input feature map derived from the one or more input images, and applying a spatial transformation to the input feature map to generate a transformed feature map, comprising: processing the input feature map to generate spatial transformation parameters for the spatial transformation, and sampling from the input feature map in accordance with the spatial transformation parameters to generate the transformed feature map.

公开(公告)号：US20240330701A1

公开(公告)日：2024-10-03

申请号：US18577484

申请日：2022-07-27

Applicant: DeepMind Technologies Limited

Inventor： Maxwell Elliot Jaderberg ,  Wojciech Czarnecki

IPC: G06N3/092 ,  G06N3/0985

CPC classification number: G06N3/092 ,  G06N3/0985

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for raining an agent neural network for use in controlling an agent to perform a plurality of tasks. One of the methods includes maintaining population data specifying a population of one or more candidate agent neural networks; and training each candidate agent neural network on a respective set of one or more tasks to update the parameter values of the parameters of the candidate agent neural networks in the population data, the training comprising, for each candidate agent neural network: obtaining data identifying a candidate task; obtaining data specifying a control policy for the candidate task; determining whether to train the candidate agent neural network on the candidate task; and in response to determining to train the candidate agent neural network on the candidate task, training the candidate agent neural network on the candidate task.

公开(公告)号：US11907821B2

公开(公告)日：2024-02-20

申请号：US16586236

申请日：2019-09-27

Applicant: DeepMind Technologies Limited

Inventor： Ang Li ,  Valentin Clement Dalibard ,  David Budden ,  Ola Spyra ,  Maxwell Elliot Jaderberg ,  Timothy James Alexander Harley ,  Sagi Perel ,  Chenjie Gu ,  Pramod Gupta

IPC: G06N20/20 ,  G06F16/901 ,  G06N5/04

CPC classification number: G06N20/20 ,  G06F16/9024 ,  G06N5/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. A method includes: maintaining a plurality of training sessions; assigning, to each worker of one or more workers, a respective training session of the plurality of training sessions; repeatedly performing operations until meeting one or more termination criteria, the operations comprising: receiving an updated training session from a respective worker of the one or more workers, selecting a second training session, selecting, based on comparing the updated training session and the second training session using a fitness evaluation function, either the updated training session or the second training session as a parent training session, generating a child training session from the selected parent training session, and assigning the child training session to an available worker, and selecting a candidate model to be a trained model for the machine learning model.

公开(公告)号：US20230244936A1

公开(公告)日：2023-08-03

申请号：US18131567

申请日：2023-04-06

Applicant: DeepMind Technologies Limited

Inventor： David Silver ,  Oriol Vinyals ,  Maxwell Elliot Jaderberg

IPC: G06N3/08 ,  H04L9/40 ,  G06F18/214

CPC classification number: G06N3/08 ,  H04L63/205 ,  G06F18/214

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a policy neural network having a plurality of policy parameters and used to select actions to be performed by an agent to control the agent to perform a particular task while interacting with one or more other agents in an environment. In one aspect, the method includes: maintaining data specifying a pool of candidate action selection policies; maintaining data specifying respective matchmaking policy; and training the policy neural network using a reinforcement learning technique to update the policy parameters. The policy parameters define policies to be used in controlling the agent to perform the particular task.