公开(公告)号：US20240013769A1

公开(公告)日：2024-01-11

申请号：US18038631

申请日：2021-11-22

Applicant: DeepMind Technologies Limited

Inventor： Ian Michael Gemp ,  Yoram Bachrach ,  Roma Patel ,  Christopher James Dyer

IPC: G10L13/047 ,  G10L13/08

CPC classification number: G10L13/047 ,  G10L13/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting an input vocabulary for a machine learning model using power indices. One of the methods includes computing a respective score for each of a plurality of text tokens in an initial vocabulary and then selecting the text tokens in the input vocabulary based on the respective scores.

公开(公告)号：US20230076192A1

公开(公告)日：2023-03-09

申请号：US17798111

申请日：2021-02-05

Applicant: DeepMind Technologies Limited

Inventor： Ian Michael Gemp

IPC: G06N3/08 ,  G06Q10/04 ,  G06Q50/06

Abstract: Machine learning techniques for multi-agent systems in which agents interact whilst performing their respective tasks. The techniques enable agents to learn to cooperate with one another, in particular by mixing incentives, in a way that improves their collective efficiency.

公开(公告)号：US20220374683A1

公开(公告)日：2022-11-24

申请号：US17668050

申请日：2022-02-09

Applicant: DeepMind Technologies Limited

Inventor： Thomas Edward Eccles ,  Ian Michael Gemp ,  János Kramár ,  Marta Garnelo Abellanas ,  Dan Rosenbaum ,  Yoram Bachrach ,  Thore Kurt Hartwig Graepel

IPC: G06N3/04 ,  G06K9/62

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting an optimal feature point in a continuous domain for a group of agents. A computer-implemented system obtains, for each of a plurality of agents, respective training data that comprises a respective utility score for each of a plurality of discrete points in the continuous domain. The system trains, for each of the plurality of agents and on the respective training data for the agents, a respective neural network that is configured to receive an input comprising a point in the continuous domain and to generate as output a predicted utility score for the agent at the point. And the system identifies the optimal point by optimizing an approximation of the shared outcome function that is defined by, for any given point in the continuous domain, a combination of the predicted utility scores generated by the respective neural networks for each of the plurality of agents by processing an input comprising the given point.

公开(公告)号：US20250094676A1

公开(公告)日：2025-03-20

申请号：US18892217

申请日：2024-09-20

Applicant: DeepMind Technologies Limited

Inventor： Ian Michael Gemp ,  Yoram Bachrach

IPC: G06F30/27

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating suggested communications during a multi-agent interaction using a language model neural network.

公开(公告)号：US20240086745A1

公开(公告)日：2024-03-14

申请号：US18275045

申请日：2022-02-07

Applicant: DeepMind Technologies Limited

Inventor： Ian Michael Gemp ,  Brian McWilliams

IPC: G06N7/01

CPC classification number: G06N7/01

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining principal components of a data set using multi-agent interactions. One of the methods includes obtaining initial estimates for a plurality of principal components of a data set; and generating a final estimate for each principal component by repeatedly performing operations comprising: generating a reward estimate using the current estimate of the principal component, wherein the reward estimate is larger if the current estimate of the principal component captures more variance in the data set; generating, for each parent principal component of the principal component, a punishment estimate, wherein the punishment estimate is larger if the current estimate of the principal component and the current estimate of the parent principal component are not orthogonal; and updating the current estimate of the principal component according to a difference between the reward estimate and the punishment estimates.