公开(公告)号：US20230244452A1

公开(公告)日：2023-08-03

申请号：US18105211

申请日：2023-02-02

Applicant: DeepMind Technologies Limited

Inventor： Yujia Li ,  David Hugo Choi ,  Junyoung Chung ,  Nathaniel Arthur Kushman ,  Julian Schrittwieser ,  Rémi Leblond ,  Thomas Edward Eccles ,  James Thomas Keeling ,  Felix Axel Gimeno Gil ,  Agustín Matías Dal Lago ,  Thomas Keisuke Hubert ,  Peter Choy ,  Cyprien de Masson d'Autume ,  Esme Sutherland Robson ,  Oriol Vinyals

IPC: G06F8/30

CPC classification number: G06F8/30

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating computer code using neural networks. One of the methods includes receiving description data describing a computer programming task; receiving a first set of inputs for the computer programming task; generating a plurality of candidate computer programs by sampling a plurality of output sequences from a set of one or more generative neural networks; for each candidate computer program in a subset of the candidate computer programs and for each input in the first set: executing the candidate computer program on the input to generate an output; and selecting, from the candidate computer programs, one or more computer programs as synthesized computer programs for performing the computer programming task based at least in part on the outputs generated by executing the candidate computer programs in the subset on the inputs in the first set of inputs.

公开(公告)号：US20240127045A1

公开(公告)日：2024-04-18

申请号：US17959210

申请日：2022-10-03

Applicant: DeepMind Technologies Limited

Inventor： Thomas Keisuke Hubert ,  Shih-Chieh Huang ,  Alexander Novikov ,  Alhussein Fawzi ,  Bernardino Romera-Paredes ,  David Silver ,  Demis Hassabis ,  Grzegorz Michal Swirszcz ,  Julian Schrittwieser ,  Pushmeet Kohli ,  Mohammadamin Barekatain ,  Matej Balog ,  Francisco Jesus Rodriguez Ruiz

IPC: G06N3/08 ,  G06N3/063

CPC classification number: G06N3/08 ,  G06N3/063

Abstract: A method performed by one or more computers for obtaining an optimized algorithm that (i) is functionally equivalent to a target algorithm and (ii) optimizes one or more target properties when executed on a target set of one or more hardware devices. The method includes: initializing a target tensor representing the target algorithm; generating, using a neural network having a plurality of network parameters, a tensor decomposition of the target tensor that parametrizes a candidate algorithm; generating target property values for each of the target properties when executing the candidate algorithm on the target set of hardware devices; determining a benchmarking score for the tensor decomposition based on the target property values of the candidate algorithm; generating a training example from the tensor decomposition and the benchmarking score; and storing, in a training data store, the training example for use in updating the network parameters of the neural network.

公开(公告)号：US20240267532A1

公开(公告)日：2024-08-08

申请号：US18565008

申请日：2022-05-30

Applicant: DeepMind Technologies Limited

Inventor： Anton Zhernov ,  Chenjie Gu ,  Daniel J. Mankowitz ,  Julian Schrittwieser ,  Amol Balkishan Mandhane ,  Mary Elizabeth Rauh ,  Miaosen Wang ,  Thomas Keisuke Hubert

IPC: H04N19/149 ,  H04N19/172

CPC classification number: H04N19/149 ,  H04N19/172

Abstract: Systems and methods for training rate control neural networks through reinforcement learning. During training, reward values for training examples are generated from the current performance of the rate control neural network in encoding the video in the training example and the historical performance of the rate control neural network in encoding the video in the training example.

公开(公告)号：US20230073326A1

公开(公告)日：2023-03-09

申请号：US17794797

申请日：2021-01-28

Applicant: DeepMind Technologies Limited

Inventor： Julian Schrittwieser ,  Ioannis Antonoglou ,  Thomas Keisuke Hubert

IPC: G06N7/00 ,  G06N5/00 ,  G06K9/62

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting actions to be performed by an agent interacting with an environment to cause the agent to perform a task. One of the methods includes: receiving a current observation characterizing a current environment state of the environment; performing a plurality of planning iterations to generate plan data that indicates a respective value to performing the task of the agent performing each of the set of actions in the environment and starting from the current environment state, wherein performing each planning iteration comprises selecting a sequence of actions to be performed by the agent starting from the current environment state based on outputs generated by a dynamics model and a prediction model; and selecting, from the set of actions, an action to be performed by the agent in response to the current observation based on the plan data.