公开(公告)号：US20250147810A1

公开(公告)日：2025-05-08

申请号：US18936711

申请日：2024-11-04

Applicant: DeepMind Technologies Limited

Inventor： Bernardino Romera-Paredes ,  Alexander Novikov ,  Mohammadamin Barekatain ,  Matej Balog ,  Pawan Kumar Mudigonda ,  Emilien Dupont ,  Francisco Jesus Rodriguez Ruiz ,  Alhussein Fawzi

IPC: G06F9/50

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for scheduling jobs across a plurality of computational resources. Scheduling jobs (e.g., compute jobs) on a plurality of computational resources (e.g., a cluster that includes physical machines, virtual machines or both) can include assigning jobs to computational resources using respective scores for the computational resources that take into account several attributes, including central processing unit (CPU) requirements, memory requirements, and availability. That is, by generating a score that more accurately reflects the likelihood that a given computational resource is the optimal computational resource to place a given job, the resulting job schedule significantly minimizes idle time of the set of computational resources and enhances the throughput of completed jobs.

公开(公告)号：US11775830B2

公开(公告)日：2023-10-03

申请号：US18079791

申请日：2022-12-12

Applicant: DeepMind Technologies Limited

Inventor： Chongli Qin ,  Sven Adrian Gowal ,  Soham De ,  Robert Stanforth ,  James Martens ,  Krishnamurthy Dvijotham ,  Dilip Krishnan ,  Alhussein Fawzi

IPC: G06N3/08 ,  G06V10/82 ,  G06F18/214 ,  G06F18/2135 ,  G06V10/764 ,  G06V10/774

CPC classification number: G06N3/08 ,  G06F18/214 ,  G06F18/21355 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes processing each training input using the neural network and in accordance with the current values of the network parameters to generate a network output for the training input; computing a respective loss for each of the training inputs by evaluating a loss function; identifying, from a plurality of possible perturbations, a maximally non-linear perturbation; and determining an update to the current values of the parameters of the neural network by performing an iteration of a neural network training procedure to decrease the respective losses for the training inputs and to decrease the non-linearity of the loss function for the identified maximally non-linear perturbation.

公开(公告)号：US20230252286A1

公开(公告)日：2023-08-10

申请号：US18079791

申请日：2022-12-12

Applicant: DeepMind Technologies Limited

Inventor： Chongli Qin ,  Sven Adrian Gowal ,  Soham De ,  Robert Stanforth ,  James Martens ,  Krishnamurthy Dvijotham ,  Dilip Krishnan ,  Alhussein Fawzi

IPC: G06N3/08 ,  G06V10/82 ,  G06F18/214 ,  G06F18/2135 ,  G06V10/764 ,  G06V10/774

CPC classification number: G06N3/08 ,  G06V10/82 ,  G06F18/214 ,  G06F18/21355 ,  G06V10/764 ,  G06V10/774

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes processing each training input using the neural network and in accordance with the current values of the network parameters to generate a network output for the training input; computing a respective loss for each of the training inputs by evaluating a loss function; identifying, from a plurality of possible perturbations, a maximally non-linear perturbation; and determining an update to the current values of the parameters of the neural network by performing an iteration of a neural network training procedure to decrease the respective losses for the training inputs and to decrease the non-linearity of the loss function for the identified maximally non-linear perturbation.

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：US11526755B2

公开(公告)日：2022-12-13

申请号：US16882332

申请日：2020-05-22

Applicant: DeepMind Technologies Limited

Inventor： Chongli Qin ,  Sven Adrian Gowal ,  Soham De ,  Robert Stanforth ,  James Martens ,  Krishnamurthy Dvijotham ,  Dilip Krishnan ,  Alhussein Fawzi

IPC: G06N3/08 ,  G06K9/62 ,  G06V10/82

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes processing each training input using the neural network and in accordance with the current values of the network parameters to generate a network output for the training input; computing a respective loss for each of the training inputs by evaluating a loss function; identifying, from a plurality of possible perturbations, a maximally non-linear perturbation; and determining an update to the current values of the parameters of the neural network by performing an iteration of a neural network training procedure to decrease the respective losses for the training inputs and to decrease the non-linearity of the loss function for the identified maximally non-linear perturbation.