公开(公告)号：US20210383228A1

公开(公告)日：2021-12-09

申请号：US17338974

申请日：2021-06-04

Applicant: DeepMind Technologies Limited

Inventor： Petar Velickovic ,  Charles Blundell ,  Oriol Vinyals ,  Razvan Pascanu ,  Lars Buesing ,  Matthew Overlan

IPC: G06N3/08 ,  G06N3/04 ,  G06F16/23 ,  G06F16/901

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating prediction outputs characterizing a set of entities. In one aspect, a method comprises: obtaining data defining a graph, comprising: (i) a set of nodes, wherein each node represents a respective entity from the set of entities, (ii) a current set of edges, wherein each edge connects a pair of nodes, and (iii) a respective current embedding of each node; at each of a plurality of time steps: updating the respective current embedding of each node, comprising processing data defining the graph using a graph neural network; and updating the current set of edges based at least in part on the updated embeddings of the nodes; and at one or more of the plurality of time steps: generating a prediction output characterizing the set of entities based on the current embeddings of the nodes.

公开(公告)号：US20220383074A1

公开(公告)日：2022-12-01

申请号：US17829204

申请日：2022-05-31

Applicant: DeepMind Technologies Limited

Inventor： Heiko Strathmann ,  Mohammadamin Barekatain ,  Charles Blundell ,  Petar Velickovic

IPC: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing persistent message passing using graph neural networks.

公开(公告)号：US20240256879A1

公开(公告)日：2024-08-01

申请号：US18423239

申请日：2024-01-25

Applicant: DeepMind Technologies Limited

Inventor： Beatrice Bevilacqua ,  Petar Velickovic ,  Jovana Mitrovic ,  Kyriacos Nikiforou ,  Ioana Bica ,  Borja Ibarz Gabardos

IPC: G06N3/0895

CPC classification number: G06N3/0895

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network to perform an algorithmic task. According to one aspect, there is provided a method comprising: obtaining an input dataset; generating a first augmented dataset and a second augmented dataset, wherein for both the first augmented dataset and the second augmented dataset: applying the computational algorithm to the augmented dataset causes the same computational operations to be performed at a target computational step as would be performed by applying the computational algorithm to the input dataset; processing the first augmented dataset and the second augmented dataset using the neural network, comprising, for each augmented dataset: generating an intermediate representation of the augmented dataset at an intermediate layer of the neural network; and training the neural network on an objective function, wherein the objective function comprises a self-supervised loss term.