DeepMind Technologies Limited patent applications on May 30th, 2024

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Patent Applications by DeepMind Technologies Limited on May 30th, 2024

DeepMind Technologies Limited: 4 patent applications

DeepMind Technologies Limited has applied for patents in the areas of G06N3/084 (4), G06N3/08 (4), G01W1/10 (2), G06N3/0475 (2), G06N3/04 (2)

With keywords such as: neural, network, computer, methods, node, output, current, representation, radar, and respective in patent application abstracts.



Patent Applications by DeepMind Technologies Limited

20240176045.NOWCASTING USING GENERATIVE NEURAL NETWORKS_simplified_abstract_(deepmind technologies limited)

Inventor(s): Suman Ravuri of London (GB) for deepmind technologies limited, Karel Lenc of London (GB) for deepmind technologies limited, Piotr Wojciech Mirowski of London (GB) for deepmind technologies limited, Remi Roger Alain Paul Lam of London (GB) for deepmind technologies limited, Matthew James Willson of London (GB) for deepmind technologies limited, Andrew Brock of London (GB) for deepmind technologies limited

IPC Code(s): G01W1/10, G06N3/0475

CPC Code(s):



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.


20240176982.TRAINING GRAPH NEURAL NETWORKS USING A DE-NOISING OBJECTIVE_simplified_abstract_(deepmind technologies limited)

Inventor(s): Jonathan William Godwin of London (GB) for deepmind technologies limited, Peter William Battaglia of London (GB) for deepmind technologies limited, Kevin Michael Schaarschmidt of Cambridge (GB) for deepmind technologies limited, Alvaro Sanchez of London (GB) for deepmind technologies limited

IPC Code(s): G06N3/04, G06N3/084

CPC Code(s):



Abstract: methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network that includes one or more graph neural network layers. in one aspect, a method comprises: generating data defining a graph, comprising: generating a respective final feature representation for each node, wherein, for each of one or more of the nodes, the respective final feature representation is a modified feature representation that is generated from a respective feature representation for the node using respective noise; processing the data defining the graph using one or more of the graph neural network layers of the neural network to generate a respective updated node embedding of each node; and processing, for each of one or more of the nodes having modified feature representations, the updated node embedding of the node to generate a respective de-noising prediction for the node.


20240177001.NEURAL PROGRAMMING_simplified_abstract_(deepmind technologies limited)

Inventor(s): Scott Ellison Reed of Atlanta GA (US) for deepmind technologies limited, Joao Ferdinando Gomes de Freitas of London (GB) for deepmind technologies limited

IPC Code(s): G06N3/08, G06N3/044, G06N20/00

CPC Code(s):



Abstract: methods, systems, and apparatus, including computer programs encoded on computer storage media, for neural programming. one of the methods includes processing a current neural network input using a core recurrent neural network to generate a neural network output; determining, from the neural network output, whether or not to end a currently invoked program and to return to a calling program from the set of programs; determining, from the neural network output, a next program to be called; determining, from the neural network output, contents of arguments to the next program to be called; receiving a representation of a current state of the environment; and generating a next neural network input from an embedding for the next program to be called and the representation of the current state of the environment.


20240177002.CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING_simplified_abstract_(deepmind technologies limited)

Inventor(s): Timothy Paul Lillicrap of London (GB) for deepmind technologies limited, Jonathan James Hunt of London (GB) for deepmind technologies limited, Alexander Pritzel of London (GB) for deepmind technologies limited, Nicolas Manfred Otto Heess of London (GB) for deepmind technologies limited, Tom Erez of London (GB) for deepmind technologies limited, Yuval Tassa of London (GB) for deepmind technologies limited, David Silver of Hitchin (GB) for deepmind technologies limited, Daniel Pieter Wierstra of London (GB) for deepmind technologies limited

IPC Code(s): G06N3/08, G06N3/006, G06N3/045, G06N3/084

CPC Code(s):



Abstract: methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an actor neural network used to select actions to be performed by an agent interacting with an environment. one of the methods includes obtaining a minibatch of experience tuples; and updating current values of the parameters of the actor neural network, comprising: for each experience tuple in the minibatch: processing the training observation and the training action in the experience tuple using a critic neural network to determine a neural network output for the experience tuple, and determining a target neural network output for the experience tuple; updating current values of the parameters of the critic neural network using errors between the target neural network outputs and the neural network outputs; and updating the current values of the parameters of the actor neural network using the critic neural network.


DeepMind Technologies Limited patent applications on May 30th, 2024