公开(公告)号：US20210150355A1

公开(公告)日：2021-05-20

申请号：US17159961

申请日：2021-01-27

Applicant: DeepMind Technologies Limited

Inventor： Marc Gendron-Bellemare ,  Jacob Lee Menick ,  Alexander Benjamin Graves ,  Koray Kavukcuoglu ,  Remi Munos

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method includes receiving training data for training the machine learning model on a plurality of tasks, where each task includes multiple batches of training data. A task is selected in accordance with a current task selection policy. A batch of training data is selected from the selected task. The machine learning model is trained on the selected batch of training data to determine updated values of the model parameters. A learning progress measure that represents a progress of the training of the machine learning model as a result of training the machine learning model on the selected batch of training data is determined. The current task selection policy is updated using the learning progress measure.

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：US20210004677A1

公开(公告)日：2021-01-07

申请号：US16767010

申请日：2019-02-11

Applicant: DeepMind Technologies Limited

Inventor： Jacob Lee Menick ,  Alexander Benjamin Graves

IPC: G06N3/08 ,  G06N20/20 ,  G06N7/00 ,  G06N3/04 ,  G06N5/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an encoder neural network, a decoder neural network, and a prior neural network, and using the trained networks for generative modeling, data compression, and data decompression. In one aspect, a method comprises: providing a given observation as input to the encoder neural network to generate parameters of an encoding probability distribution; determining an updated code for the given observation; selecting a code that is assigned to an additional observation; providing the code assigned to the additional observation as input to the prior neural network to generate parameters of a prior probability distribution; sampling latent variables from the encoding probability distribution; providing the latent variables as input to the decoder neural network to generate parameters of an observation probability distribution; and determining gradients of a loss function.

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：US20190332938A1

公开(公告)日：2019-10-31

申请号：US16508042

申请日：2019-07-10

Applicant: DeepMind Technologies Limited

Inventor： Marc Gendron-Bellemare ,  Jacob Lee Menick ,  Alexander Benjamin Graves ,  Koray Kavukcuoglu ,  Remi Munos

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method includes receiving training data for training the machine learning model on a plurality of tasks, where each task includes multiple batches of training data. A task is selected in accordance with a current task selection policy. A batch of training data is selected from the selected task. The machine learning model is trained on the selected batch of training data to determine updated values of the model parameters. A learning progress measure that represents a progress of the training of the machine learning model as a result of training the machine learning model on the selected batch of training data is determined. The current task selection policy is updated using the learning progress measure.

公开(公告)号：US20180260708A1

公开(公告)日：2018-09-13

申请号：US15977923

申请日：2018-05-11

Applicant: DeepMind Technologies Limited

Inventor： Volodymyr Mnih ,  Adrià Puigdomènech Badia ,  Alexander Benjamin Graves ,  Timothy James Alexander Harley ,  David Silver ,  Koray Kavukcuoglu

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/0454

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.