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1.发明授权公开(公告)号：US10198832B2
公开(公告)日：2019-02-05
申请号：US16022170
申请日：2018-06-28
Applicant: DeepMind Technologies Limited
Inventor： Jeffrey De Fauw , Joseph R. Ledsam , Bernardino Romera-Paredes , Stanislav Nikolov , Nenad Tomasev , Samuel Blackwell , Harry Askham , Xavier Glorot , Balaji Lakshminarayanan , Trevor Back , Mustafa Suleyman , Pearse A. Keane , Olaf Ronneberger , Julien Robert Michel Cornebise
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final classification output for an image of eye tissue. The image is provided as input to each of one or more segmentation neural networks to obtain one or more segmentation maps of the eye tissue in the image. A respective classification input is generated from each of the segmentation maps. For each of the segmentation maps, the classification input for the segmentation map is provided as input to each of one or more classification neural networks to obtain, for each segmentation map, a respective classification output from each classification neural network. A final classification output for the image is generated from the respective classification outputs for each of the segmentation maps.
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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
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.
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公开(公告)号：US11430123B2
公开(公告)日：2022-08-30
申请号：US16881775
申请日：2020-05-22
Applicant: DeepMind Technologies Limited
Inventor： Simon Kohl , Bernardino Romera-Paredes , Danilo Jimenez Rezende , Seyed Mohammadali Eslami , Pushmeet Kohli , Andrew Zisserman , Olaf Ronneberger
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a plurality of possible segmentations of an image. In one aspect, a method comprises: receiving a request to generate a plurality of possible segmentations of an image; sampling a plurality of latent variables from a latent space, wherein each latent variable is sampled from the latent space in accordance with a respective probability distribution over the latent space that is determined based on the image; generating a plurality of possible segmentations of the image, comprising, for each latent variable, processing the image and the latent variable using a segmentation neural network having a plurality of segmentation neural network parameters to generate the possible segmentation of the image; and providing the plurality of possible segmentations of the image in response to the request.
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Patent Agency Ranking
4.发明申请公开(公告)号：US20200082534A1
公开(公告)日：2020-03-12
申请号：US16565384
申请日：2019-09-09
Applicant: DeepMind Technologies Limited
Inventor： Stanislav Nikolov , Samuel Blackwell , Jeffrey De Fauw , Bernardino Romera-Paredes , Clemens Meyer , Harry Askham , Cian Hughes , Trevor Back , Joseph R. Ledsam , Olaf Ronneberger
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for segmenting a medical image. In one aspect, a method comprises: receiving a medical image that is captured using a medical imaging modality and that depicts a region of tissue in a body; and processing the medical image using a segmentation neural network to generate a segmentation output, wherein the segmentation neural network comprises a sequence of multiple encoder blocks, wherein: each encoder block is a residual neural network block comprising one or more two-dimensional convolutional neural network layers, one or more three-dimensional convolutional neural network layers, or both, and each encoder block is configured to process a respective encoder block input to generate a respective encoder block output wherein a spatial resolution of the encoder block output is lower than a spatial resolution of the encoder block input.
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5.发明申请公开(公告)号：US20190139270A1
公开(公告)日：2019-05-09
申请号：US16236045
申请日：2018-12-28
Applicant: DeepMind Technologies Limited
Inventor： Jeffrey De Fauw , Joseph R. Ledsam , Bernardino Romera-Paredes , Stanislav Nikolov , Nenad Tomasev , Samuel Blackwell , Harry Askham , Xavier Glorot , Balaji Lakshminarayanan , Trevor Back , Mustafa Suleyman , Pearse A. Keane , Olaf Ronneberger , Julien Robert Michel Cornebise
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final classification output for an image of eye tissue. The image is provided as input to each of one or more segmentation neural networks to obtain one or more segmentation maps of the eye tissue in the image. A respective classification input is generated from each of the segmentation maps. For each of the segmentation maps, the classification input for the segmentation map is provided as input to each of one or more classification neural networks to obtain, for each segmentation map, a respective classification output from each classification neural network. A final classification output for the image is generated from the respective classification outputs for each of the segmentation maps.
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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.
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公开(公告)号：US20200372654A1
公开(公告)日：2020-11-26
申请号：US16881775
申请日：2020-05-22
Applicant: DeepMind Technologies Limited
Inventor： Simon Kohl , Bernardino Romera-Paredes , Danilo Jimenez Rezende , Seyed Mohammadali Eslami , Pushmeet Kohli , Andrew Zisserman , Olaf Ronneberger
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a plurality of possible segmentations of an image. In one aspect, a method comprises: receiving a request to generate a plurality of possible segmentations of an image; sampling a plurality of latent variables from a latent space, wherein each latent variable is sampled from the latent space in accordance with a respective probability distribution over the latent space that is determined based on the image; generating a plurality of possible segmentations of the image, comprising, for each latent variable, processing the image and the latent variable using a segmentation neural network having a plurality of segmentation neural network parameters to generate the possible segmentation of the image; and providing the plurality of possible segmentations of the image in response to the request.
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8.发明申请公开(公告)号：US20190005684A1
公开(公告)日：2019-01-03
申请号：US16022170
申请日：2018-06-28
Applicant: DeepMind Technologies Limited
Inventor： Jeffrey De Fauw , Joseph R. Ledsam , Bernardino Romera-Paredes , Stanislav Nikolov , Nenad Tomasev , Samuel Blackwell , Harry Askham , Xavier Glorot , Balaji Lakshminarayanan , Trevor Back , Mustafa Suleyman , Pearse A. Keane , Olaf Ronneberger , Julien Robert Michel Cornebise
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final classification output for an image of eye tissue. The image is provided as input to each of one or more segmentation neural networks to obtain one or more segmentation maps of the eye tissue in the image. A respective classification input is generated from each of the segmentation maps. For each of the segmentation maps, the classification input for the segmentation map is provided as input to each of one or more classification neural networks to obtain, for each segmentation map, a respective classification output from each classification neural network. A final classification output for the image is generated from the respective classification outputs for each of the segmentation maps.
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