公开(公告)号：US20240346285A1

公开(公告)日：2024-10-17

申请号：US18607777

申请日：2024-03-18

Applicant: DeepMind Technologies Limited

Inventor： Aaron Gerard Antonius van den Oord ,  Karen Simonyan ,  Oriol Vinyals

IPC: G06N3/04 ,  G06N3/045 ,  G06N3/08 ,  G06N3/088 ,  G10L13/00

CPC classification number: G06N3/04 ,  G06N3/045 ,  G06N3/08 ,  G06N3/088 ,  G10L13/00

Abstract: A feedforward generative neural network that generates an output example that includes multiple output samples of a particular type in a single neural network inference. Optionally, the generation may be conditioned on a context input. For example, the feedforward generative neural network may generate a speech waveform that is a verbalization of an input text segment conditioned on linguistic features of the text segment.

公开(公告)号：US20240273333A1

公开(公告)日：2024-08-15

申请号：US18640741

申请日：2024-04-19

Applicant: DeepMind Technologies Limited

Inventor： Aaron Gerard Antonius van den Oord ,  Yazhe Li ,  Oriol Vinyals

IPC: G06N3/006 ,  G06F17/16 ,  G06F18/22 ,  G06N3/045 ,  G06N3/048 ,  G06N3/08 ,  G06V10/764 ,  G06V10/77 ,  G06V10/82

CPC classification number: G06N3/006 ,  G06F17/16 ,  G06F18/22 ,  G06N3/045 ,  G06N3/048 ,  G06N3/08 ,  G06V10/764 ,  G06V10/7715 ,  G06V10/82

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an encoder neural network that is configured to process an input observation to generate a latent representation of the input observation. In one aspect, a method includes: obtaining a sequence of observations; for each observation in the sequence of observations, processing the observation using the encoder neural network to generate a latent representation of the observation; for each of one or more given observations in the sequence of observations: generating a context latent representation of the given observation; and generating, from the context latent representation of the given observation, a respective estimate of the latent representations of one or more particular observations that are after the given observation in the sequence of observations.

公开(公告)号：US11995528B2

公开(公告)日：2024-05-28

申请号：US18090243

申请日：2022-12-28

Applicant: DeepMind Technologies Limited

Inventor： Aaron Gerard Antonius van den Oord ,  Yazhe Li ,  Oriol Vinyals

IPC: G06N3/006 ,  G06F17/16 ,  G06F18/22 ,  G06N3/045 ,  G06N3/048 ,  G06N3/08 ,  G06V10/764 ,  G06V10/77 ,  G06V10/82

CPC classification number: G06N3/006 ,  G06F17/16 ,  G06F18/22 ,  G06N3/045 ,  G06N3/048 ,  G06N3/08 ,  G06V10/764 ,  G06V10/7715 ,  G06V10/82

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an encoder neural network that is configured to process an input observation to generate a latent representation of the input observation. In one aspect, a method includes: obtaining a sequence of observations; for each observation in the sequence of observations, processing the observation using the encoder neural network to generate a latent representation of the observation; for each of one or more given observations in the sequence of observations: generating a context latent representation of the given observation; and generating, from the context latent representation of the given observation, a respective estimate of the latent representations of one or more particular observations that are after the given observation in the sequence of observations.

公开(公告)号：US11948066B2

公开(公告)日：2024-04-02

申请号：US17375250

申请日：2021-07-14

Applicant: DeepMind Technologies Limited

Inventor： Aaron Gerard Antonius van den Oord ,  Sander Etienne Lea Dieleman ,  Nal Emmerich Kalchbrenner ,  Karen Simonyan ,  Oriol Vinyals ,  Lasse Espeholt

IPC: G06N3/04 ,  G06F40/279 ,  G06F40/44 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/084 ,  G10L13/04 ,  G10L13/08 ,  G10L15/16 ,  G10L25/30 ,  G06F17/18

CPC classification number: G06N3/047 ,  G06F40/279 ,  G06F40/44 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084 ,  G10L13/04 ,  G10L13/086 ,  G10L15/16 ,  G10L25/30 ,  G06F17/18 ,  G10H2250/311

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing sequences using convolutional neural networks. One of the methods includes, for each of the time steps: providing a current sequence of audio data as input to a convolutional subnetwork, wherein the current sequence comprises the respective audio sample at each time step that precedes the time step in the output sequence, and wherein the convolutional subnetwork is configured to process the current sequence of audio data to generate an alternative representation for the time step; and providing the alternative representation for the time step as input to an output layer, wherein the output layer is configured to: process the alternative representation to generate an output that defines a score distribution over a plurality of possible audio samples for the time step.

公开(公告)号：US20240104353A1

公开(公告)日：2024-03-28

申请号：US18274748

申请日：2022-02-08

Applicant: DeepMind Technologies Limited

Inventor： Rémi Bertrand Francis Leblond ,  Jean-Baptiste Alayrac ,  Laurent Sifre ,  Miruna Pîslar ,  Jean-Baptiste Lespiau ,  Ioannis Antonoglou ,  Karen Simonyan ,  David Silver ,  Oriol Vinyals

IPC: G06N3/0455

CPC classification number: G06N3/0455

Abstract: A computer-implemented method for generating an output token sequence from an input token sequence. The method combines a look ahead tree search, such as a Monte Carlo tree search, with a sequence-to-sequence neural network system. The sequence-to-sequence neural network system has a policy output defining a next token probability distribution, and may include a value neural network providing a value output to evaluate a sequence. An initial partial output sequence is extended using the look ahead tree search guided by the policy output and, in implementations, the value output, of the sequence-to-sequence neural network system until a complete output sequence is obtained.

公开(公告)号：US11907853B2

公开(公告)日：2024-02-20

申请号：US16759567

申请日：2018-10-26

Applicant: DeepMind Technologies Limited

Inventor： Chrisantha Thomas Fernando ,  Karen Simonyan ,  Koray Kavukcuoglu ,  Hanxiao Liu ,  Oriol Vinyals

IPC: G06N3/086 ,  G06N3/045 ,  G06F17/15 ,  G06F16/901

CPC classification number: G06N3/086 ,  G06F16/9024 ,  G06N3/045 ,  G06F17/15

Abstract: A computer-implemented method for automatically determining a neural network architecture represents a neural network architecture as a data structure defining a hierarchical set of directed acyclic graphs in multiple levels. Each graph has an input, an output, and a plurality of nodes between the input and the output. At each level, a corresponding set of the nodes are connected pairwise by directed edges which indicate operations performed on outputs of one node to generate an input to another node. Each level is associated with a corresponding set of operations. At a lowest level, the operations associated with each edge are selected from a set of primitive operations. The method includes repeatedly generating new sample neural network architectures, and evaluating their fitness. The modification is performed by selecting a level, selecting two nodes at that level, and modifying, removing or adding an edge between those nodes according to operations associated with lower levels of the hierarchy.

公开(公告)号：US20230244907A1

公开(公告)日：2023-08-03

申请号：US18102985

申请日：2023-01-30

Applicant: DeepMind Technologies Limited

Inventor： Curtis Glenn-Macway Hawthorne ,  Andrew Coulter Jaegle ,  Catalina-Codruta Cangea ,  Sebastian Borgeaud Dit Avocat ,  Charlie Thomas Curtis Nash ,  Mateusz Malinowski ,  Sander Etienne Lea Dieleman ,  Oriol Vinyals ,  Matthew Botvinick ,  Ian Stuart Simon ,  Hannah Rachel Sheahan ,  Neil Zeghidour ,  Jean-Baptiste Alayrac ,  Joao Carreira ,  Jesse Engel

IPC: G06N3/044

CPC classification number: G06N3/044

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a sequence of data elements that includes a respective data element at each position in a sequence of positions. In one aspect, a method includes: for each position after a first position in the sequence of positions: obtaining a current sequence of data element embeddings that includes a respective data element embedding of each data element at a position that precedes the current position, obtaining a sequence of latent embeddings, and processing: (i) the current sequence of data element embeddings, and (ii) the sequence of latent embeddings, using a neural network to generate the data element at the current position. The neural network includes a sequence of neural network blocks including: (i) a cross-attention block, (ii) one or more self-attention blocks, and (iii) an output block.

公开(公告)号：US11663441B2

公开(公告)日：2023-05-30

申请号：US16586437

申请日：2019-09-27

Applicant: DeepMind Technologies Limited

Inventor： Scott Ellison Reed ,  Yusuf Aytar ,  Ziyu Wang ,  Tom Paine ,  Sergio Gomez Colmenarejo ,  David Budden ,  Tobias Pfaff ,  Aaron Gerard Antonius van den Oord ,  Oriol Vinyals ,  Alexander Novikov

IPC: G06N3/006 ,  G06F17/16 ,  G06N3/08 ,  G06F18/22 ,  G06N3/045 ,  G06N3/048 ,  G06V10/764 ,  G06V10/77 ,  G06V10/82

CPC classification number: G06N3/006 ,  G06F17/16 ,  G06F18/22 ,  G06N3/045 ,  G06N3/048 ,  G06N3/08 ,  G06V10/764 ,  G06V10/7715 ,  G06V10/82

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection policy neural network, wherein the action selection policy neural network is configured to process an observation characterizing a state of an environment to generate an action selection policy output, wherein the action selection policy output is used to select an action to be performed by an agent interacting with an environment. In one aspect, a method comprises: obtaining an observation characterizing a state of the environment subsequent to the agent performing a selected action; generating a latent representation of the observation; processing the latent representation of the observation using a discriminator neural network to generate an imitation score; determining a reward from the imitation score; and adjusting the current values of the action selection policy neural network parameters based on the reward using a reinforcement learning training technique.

公开(公告)号：US20230061411A1

公开(公告)日：2023-03-02

申请号：US17410689

申请日：2021-08-24

Applicant: DeepMind Technologies Limited

Inventor： Tom Erez ,  Alexander Novikov ,  Emilio Parisotto ,  Jack William Rae ,  Konrad Zolna ,  Misha Man Ray Denil ,  Joao Ferdinando Gomes de Freitas ,  Oriol Vinyals ,  Scott Ellison Reed ,  Sergio Gomez ,  Ashley Deloris Edwards ,  Jacob Bruce ,  Gabriel Barth-Maron

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent to interact with an environment using an action selection neural network. In one aspect, a method comprises, at each time step in a sequence of time steps: generating a current representation of a state of a task being performed by the agent in the environment as of the current time step as a sequence of data elements; autoregressively generating a sequence of data elements representing a current action to be performed by the agent at the current time step; and after autoregressively generating the sequence of data elements representing the current action, causing the agent to perform the current action at the current time step.

公开(公告)号：US11069345B2

公开(公告)日：2021-07-20

申请号：US16719424

申请日：2019-12-18

Applicant: DeepMind Technologies Limited

Inventor： Aaron Gerard Antonius van den Oord ,  Sander Etienne Lea Dieleman ,  Nal Emmerich Kalchbrenner ,  Karen Simonyan ,  Oriol Vinyals ,  Lasse Espeholt

IPC: G10L15/16 ,  G06N3/04 ,  G06N3/08 ,  G10L15/02 ,  G10L15/22

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing speech recognition by generating a neural network output from an audio data input sequence, where the neural network output characterizes words spoken in the audio data input sequence. One of the methods includes, for each of the audio data inputs, providing a current audio data input sequence that comprises the audio data input and the audio data inputs preceding the audio data input in the audio data input sequence to a convolutional subnetwork comprising a plurality of dilated convolutional neural network layers, wherein the convolutional subnetwork is configured to, for each of the plurality of audio data inputs: receive the current audio data input sequence for the audio data input, and process the current audio data input sequence to generate an alternative representation for the audio data input.