公开(公告)号：US10824946B2

公开(公告)日：2020-11-03

申请号：US16511496

申请日：2019-07-15

Applicant: DeepMind Technologies Limited

Inventor： Meire Fortunato ,  Charles Blundell ,  Oriol Vinyals

IPC: G06N3/08 ,  G06N3/04

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.

公开(公告)号：US20210004689A1

公开(公告)日：2021-01-07

申请号：US17024217

申请日：2020-09-17

Applicant: DeepMind Technologies Limited

Inventor： Meire Fortunato ,  Charles Blundell ,  Oriol Vinyals

IPC: G06N3/08 ,  G06N3/04

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.

公开(公告)号：US11836630B2

公开(公告)日：2023-12-05

申请号：US17024217

申请日：2020-09-17

Applicant: DeepMind Technologies Limited

Inventor： Meire Fortunato ,  Charles Blundell ,  Oriol Vinyals

IPC: G06N3/084 ,  G06N3/044 ,  G06N3/047

CPC classification number: G06N3/084 ,  G06N3/044 ,  G06N3/047

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.

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

IPC: G06N3/08 ,  G06N3/04

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.

公开(公告)号：US11977983B2

公开(公告)日：2024-05-07

申请号：US17020248

申请日：2020-09-14

Applicant: DeepMind Technologies Limited

Inventor： Mohammad Gheshlaghi Azar ,  Meire Fortunato ,  Bilal Piot ,  Olivier Claude Pietquin ,  Jacob Lee Menick ,  Volodymyr Mnih ,  Charles Blundell ,  Remi Munos

IPC: G06N3/084 ,  G06N3/044

CPC classification number: G06N3/084 ,  G06N3/044

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting an action to be performed by a reinforcement learning agent. The method includes obtaining an observation characterizing a current state of an environment. For each layer parameter of each noisy layer of a neural network, a respective noise value is determined. For each layer parameter of each noisy layer, a noisy current value for the layer parameter is determined from a current value of the layer parameter, a current value of a corresponding noise parameter, and the noise value. A network input including the observation is processed using the neural network in accordance with the noisy current values to generate a network output for the network input. An action is selected from a set of possible actions to be performed by the agent in response to the observation using the network output.

公开(公告)号：US20230359788A1

公开(公告)日：2023-11-09

申请号：US18027174

申请日：2021-10-01

Applicant: DeepMind Technologies Limited

Inventor： Alvaro Sanchez ,  Jonathan William Godwin ,  Rex Ying ,  Tobias Pfaff ,  Meire Fortunato ,  Peter William Battaglia

IPC: G06F30/27

CPC classification number: G06F30/27 ,  G06F2113/08

Abstract: This specification describes a simulation system that performs simulations of physical environments using a graph neural network. At each of one or more time steps in a sequence of time steps, the system can process a representation of a current state of the physical environment at the current time step using the graph neural network to generate a prediction of a next state of the physical environment at the next time step. Some implementations of the system are adapted for hardware GLOBAL acceleration. As well as performing simulations, the system can be used to predict physical quantities based on measured real-world data. Implementations of the system are differentiable and can also be used for design optimization, and for optimal control tasks.

公开(公告)号：US20210065012A1

公开(公告)日：2021-03-04

申请号：US17020248

申请日：2020-09-14

Applicant: DeepMind Technologies Limited

Inventor： Mohammad Gheshlaghi Azar ,  Meire Fortunato ,  Bilal Piot ,  Olivier Claude Pietquin ,  Jacob Lee Menick ,  Volodymyr Mnih ,  Charles Blundell ,  Remi Munos

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting an action to be performed by a reinforcement learning agent. The method includes obtaining an observation characterizing a current state of an environment. For each layer parameter of each noisy layer of a neural network, a respective noise value is determined. For each layer parameter of each noisy layer, a noisy current value for the layer parameter is determined from a current value of the layer parameter, a current value of a corresponding noise parameter, and the noise value. A network input including the observation is processed using the neural network in accordance with the noisy current values to generate a network output for the network input. An action is selected from a set of possible actions to be performed by the agent in response to the observation using the network output.