公开(公告)号：US20240232580A1

公开(公告)日：2024-07-11

申请号：US18284595

申请日：2022-05-27

Applicant: DEEPMIND TECHNOLOGIES LIMITED

Inventor： Andrew Coulter Jaegle ,  Jean-Baptiste Alayrac ,  Sebastian Borgeaud Dit Avocat ,  Catalin-Dumitru Ionescu ,  Carl Doersch ,  Fengning Ding ,  Oriol Vinyals ,  Olivier Jean Hénaff ,  Skanda Kumar Koppula ,  Daniel Zoran ,  Andrew Brock ,  Evan Gerard Shelhamer ,  Andrew Zisserman ,  Joao Carreira

IPC: G06N3/0455

CPC classification number: G06N3/0455

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a network output using a neural network. In one aspect, a method comprises: obtaining: (i) a network input to a neural network, and (ii) a set of query embeddings; processing the network input using the neural network to generate a network output that comprises a respective dimension corresponding to each query embedding in the set of query embeddings, comprising: processing the network input using an encoder block of the neural network to generate a representation of the network input as a set of latent embeddings; and processing: (i) the set of latent embeddings, and (ii) the set of query embeddings, using a cross-attention block that generates each dimension of the network output by cross-attention of a corresponding query embedding over the set of latent embeddings.

公开(公告)号：US20240176045A1

公开(公告)日：2024-05-30

申请号：US18277729

申请日：2022-02-16

Applicant: DeepMind Technologies Limited

Inventor： Suman Ravuri ,  Karel Lenc ,  Piotr Wojciech Mirowski ,  Remi Roger Alain Paul Lam ,  Matthew James Willson ,  Andrew Brock

IPC: G01W1/10 ,  G06N3/0475

CPC classification number: G01W1/10 ,  G06N3/0475

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for precipitation nowcasting using generative neural networks. One of the methods includes obtaining a context temporal sequence of a plurality of context radar fields characterizing a real-world location, each context radar field characterizing the weather in the real-world location at a corresponding preceding time point; sampling a set of one or more latent inputs by sampling values from a specified distribution; and for each sampled latent input, processing the context temporal sequence of radar fields and the sampled latent input using a generative neural network that has been configured through training to process the temporal sequence of radar fields to generate as output a predicted temporal sequence comprising a plurality of predicted radar fields, each predicted radar field in the predicted temporal sequence characterizing the predicted weather in the real-world location at a corresponding future time point.

公开(公告)号：US20240127586A1

公开(公告)日：2024-04-18

申请号：US18275087

申请日：2022-02-02

Applicant: DeepMind Technologies Limited

Inventor： Andrew Brock ,  Soham De ,  Samuel Laurence Smith ,  Karen Simonyan

IPC: G06V10/82 ,  G06V10/776

CPC classification number: G06V10/82 ,  G06V10/776

Abstract: There is disclosed a computer-implemented method for training a neural network. The method comprises determining a gradient associated with a parameter of the neural network. The method further comprises determining a ratio of a gradient norm to parameter norm and comparing the ratio to a threshold. In response to determining that the ratio exceeds the threshold, the value of the gradient is reduced such that the ratio is equal to or below the threshold. The value of the parameter is updated based upon the reduced gradient value.