公开(公告)号：US20210383227A1

公开(公告)日：2021-12-09

申请号：US17338938

申请日：2021-06-04

Applicant: DeepMind Technologies Limited

Inventor： Jovana Mitrovic ,  Brian McWilliams

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an embedding neural network based on score distributions. In one aspect, a system comprises: generating a first and second embedding of a data element, comprising: applying a first and second transformation to the data element to generate a respective first and second version of the data element and processing the respective versions using the embedding neural network to generate the respective first and second embeddings; generating, for the data element, a respective first and respective second score distribution, comprising: processing at least the first and the second embedding to generate the first and the second score distribution, respectively; and updating the current embedding network parameter values to optimize an objective function that is based on at least the first score distribution, that encourages a similarity between: (i) the first, and (ii) the second score distribution.

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.