公开(公告)号：US20210117801A1

公开(公告)日：2021-04-22

申请号：US17093373

申请日：2020-11-09

Applicant: DeepMind Technologies Limited

Inventor： Alexander Benjamin Graves ,  Ivo Danihelka ,  Timothy James Alexander Harley ,  Malcolm Kevin Campbell Reynolds ,  Gregory Duncan Wayne

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting neural networks with an external memory. One of the systems includes a memory interface subsystem that is configured to perform operations comprising determining a respective content-based weight for each of a plurality of locations in an external memory; determining a respective allocation weight for each of the plurality of locations in the external memory; determining a respective final writing weight for each of the plurality of locations in the external memory from the respective content-based weight for the location and the respective allocation weight for the location; and writing data defined by the write vector to the external memory in accordance with the final writing weights.

公开(公告)号：US10691997B2

公开(公告)日：2020-06-23

申请号：US14977201

申请日：2015-12-21

Applicant: DeepMind Technologies Limited

Inventor： Alexander Benjamin Graves ,  Ivo Danihelka ,  Gregory Duncan Wayne

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/063

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting neural networks to generate additional outputs. One of the systems includes a neural network and a sequence processing subsystem, wherein the sequence processing subsystem is configured to perform operations comprising, for each of the system inputs in a sequence of system inputs: receiving the system input; generating an initial neural network input from the system input; causing the neural network to process the initial neural network input to generate an initial neural network output for the system input; and determining, from a first portion of the initial neural network output for the system input, whether or not to cause the neural network to generate one or more additional neural network outputs for the system input.

公开(公告)号：US20190213469A1

公开(公告)日：2019-07-11

申请号：US16241334

申请日：2019-01-07

Applicant: DeepMind Technologies Limited

Inventor： Ivo Danihelka ,  Danilo Jimenez Rezende ,  Shakir Mohamed

IPC: G06N3/04 ,  G06N3/08 ,  G06K9/62 ,  G06K9/66

CPC classification number: G06N3/0445 ,  G06K9/6257 ,  G06K9/66 ,  G06N3/0472 ,  G06N3/084

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a neural network system. In one aspect, a neural network system includes a recurrent neural network that is configured to, for each time step of a predetermined number of time steps, receive a set of latent variables for the time step and process the latent variables to update a hidden state of the recurrent neural network; and a generative subsystem that is configured to, for each time step, generate the set of latent variables for the time step and provide the set of latent variables as input to the recurrent neural network; update a hidden canvas using the updated hidden state of the recurrent neural network; and, for a last time step, generate an output image using the updated hidden canvas for the last time step.

公开(公告)号：US11790209B2

公开(公告)日：2023-10-17

申请号：US17384280

申请日：2021-07-23

Applicant: DeepMind Technologies Limited

Inventor： Karol Gregor ,  Ivo Danihelka

IPC: G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G10L25/30 ,  G10L13/02

CPC classification number: G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G10L13/02 ,  G10L25/30

Abstract: Methods, and systems, including computer programs encoded on computer storage media for generating data items. A method includes reading a glimpse from a data item using a decoder hidden state vector of a decoder for a preceding time step, providing, as input to a encoder, the glimpse and decoder hidden state vector for the preceding time step for processing, receiving, as output from the encoder, a generated encoder hidden state vector for the time step, generating a decoder input from the generated encoder hidden state vector, providing the decoder input to the decoder for processing, receiving, as output from the decoder, a generated a decoder hidden state vector for the time step, generating a neural network output update from the decoder hidden state vector for the time step, and combining the neural network output update with a current neural network output to generate an updated neural network output.

公开(公告)号：US10885426B2

公开(公告)日：2021-01-05

申请号：US15396289

申请日：2016-12-30

Applicant: DeepMind Technologies Limited

Inventor： Adam Anthony Santoro ,  Daniel Pieter Wiestra ,  Timothy Paul Lillicrap ,  Sergey Bartunov ,  Ivo Danihelka

IPC: G06N3/063 ,  G06N3/04 ,  G06N3/08 ,  G06F12/123

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting neural networks with an external memory. One of the systems includes a controller neural network that includes a Least Recently Used Access (LRUA) subsystem configured to: maintain a respective usage weight for each of a plurality of locations in the external memory, and for each of the plurality of time steps: generate a respective reading weight for each location using a read key, read data from the locations in accordance with the reading weights, generate a respective writing weight for each of the locations from a respective reading weight from a preceding time step and the respective usage weight for the location, write a write vector to the locations in accordance with the writing weights, and update the respective usage weight from the respective reading weight and the respective writing weight.

公开(公告)号：US20230419076A1

公开(公告)日：2023-12-28

申请号：US18367305

申请日：2023-09-12

Applicant: DeepMind Technologies Limited

Inventor： Karol Gregor ,  Ivo Danihelka

IPC: G06N3/04 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G10L25/30

Abstract: Methods, and systems, including computer programs encoded on computer storage media for generating data items. A method includes reading a glimpse from a data item using a decoder hidden state vector of a decoder for a preceding time step, providing, as input to a encoder, the glimpse and decoder hidden state vector for the preceding time step for processing, receiving, as output from the encoder, a generated encoder hidden state vector for the time step, generating a decoder input from the generated encoder hidden state vector, providing the decoder input to the decoder for processing, receiving, as output from the decoder, a generated a decoder hidden state vector for the time step, generating a neural network output update from the decoder hidden state vector for the time step, and combining the neural network output update with a current neural network output to generate an updated neural network output.

公开(公告)号：US20220366246A1

公开(公告)日：2022-11-17

申请号：US17763914

申请日：2020-09-24

Applicant: DeepMind Technologies Limited

Inventor： Ivo Danihelka ,  Danilo Jimenez Rezende ,  Karol Gregor ,  Georgios Papamakarios ,  Theophane Guillaume Weber

IPC: G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using an environment model to simulate state transitions of an environment being interacted with by an agent that is controlled using a policy neural network. One of the methods includes initializing an internal representation of a state of the environment at a current time point; repeatedly performing the following operations: receiving an action to be performed by the agent; generating, based on the internal representation, a predicted latent representation that is a prediction of a latent representation that would have been generated by the policy neural network by processing an observation characterizing the state of the environment corresponding to the internal representation; and updating the internal representation to simulate a state transition caused by the agent performing the received action by processing the predicted latent representation and the received action using the environment model.

公开(公告)号：US10832134B2

公开(公告)日：2020-11-10

申请号：US15374974

申请日：2016-12-09

Applicant: DeepMind Technologies Limited

Inventor： Alexander Benjamin Graves ,  Ivo Danihelka ,  Timothy James Alexander Harley ,  Malcolm Kevin Campbell Reynolds ,  Gregory Duncan Wayne

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/063

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting neural networks with an external memory. One of the systems includes a memory interface subsystem that is configured to perform operations comprising determining a respective content-based weight for each of a plurality of locations in an external memory; determining a respective allocation weight for each of the plurality of locations in the external memory; determining a respective final writing weight for each of the plurality of locations in the external memory from the respective content-based weight for the location and the respective allocation weight for the location; and writing data defined by the write vector to the external memory in accordance with the final writing weights.

公开(公告)号：US20200226446A1

公开(公告)日：2020-07-16

申请号：US16831566

申请日：2020-03-26

Applicant: DeepMind Technologies Limited

Inventor： Alexander Benjamin Graves ,  Ivo Danihelka ,  Gregory Duncan Wayne

IPC: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting neural networks with an external memory. One of the methods includes providing an output derived from a first portion of a neural network output as a system output; determining one or more sets of writing weights for each of a plurality of locations in an external memory; writing data defined by a third portion of the neural network output to the external memory in accordance with the sets of writing weights; determining one or more sets of reading weights for each of the plurality of locations in the external memory from a fourth portion of the neural network output; reading data from the external memory in accordance with the sets of reading weights; and combining the data read from the external memory with a next system input to generate the next neural network input.

公开(公告)号：US10657436B2

公开(公告)日：2020-05-19

申请号：US16241334

申请日：2019-01-07

Applicant: DeepMind Technologies Limited

Inventor： Ivo Danihelka ,  Danilo Jimenez Rezende ,  Shakir Mohamed

IPC: G06N3/04 ,  G06K9/62 ,  G06K9/66 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a neural network system. In one aspect, a neural network system includes a recurrent neural network that is configured to, for each time step of a predetermined number of time steps, receive a set of latent variables for the time step and process the latent variables to update a hidden state of the recurrent neural network; and a generative subsystem that is configured to, for each time step, generate the set of latent variables for the time step and provide the set of latent variables as input to the recurrent neural network; update a hidden canvas using the updated hidden state of the recurrent neural network; and, for a last time step, generate an output image using the updated hidden canvas for the last time step.