公开(公告)号：US20210118198A1

公开(公告)日：2021-04-22

申请号：US17114586

申请日：2020-12-08

Applicant: Google LLC

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

IPC: G06T11/00 ,  G06K9/62 ,  G06K9/46 ,  G06T7/00

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.

公开(公告)号：US20210073638A1

公开(公告)日：2021-03-11

申请号：US17099387

申请日：2020-11-16

Applicant: Google LLC

Inventor： Benjamin Kenneth Coppin ,  Mustafa Suleyman ,  Thomas Chadwick Walters ,  Timothy Mann ,  Chia-Yueh Carlton Chu ,  Martin Szummer ,  Luis Carlos Cobo Rus ,  Jean-Francois Crespo

IPC: G06N3/08 ,  G06N20/00 ,  G06F16/9535 ,  G06F16/2457 ,  G06N3/04 ,  G06N7/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a machine learning model that has been trained through reinforcement learning to select a content item. One of the methods includes receiving first data characterizing a first context in which a first content item may be presented to a first user in a presentation environment; and providing the first data as input to a long-term engagement machine learning model, the model having been trained through reinforcement learning to: receive a plurality of inputs, and process each of the plurality of inputs to generate a respective engagement score for each input that represents a predicted, time-adjusted total number of selections by the respective user of future content items presented to the respective user in the presentation environment if the respective content item is presented in the respective context.

公开(公告)号：US11954902B2

公开(公告)日：2024-04-09

申请号：US17114586

申请日：2020-12-08

Applicant: Google LLC

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

IPC: G06V10/82 ,  G06F18/21 ,  G06F18/2413 ,  G06F18/25 ,  G06T7/00 ,  G06T11/00 ,  G06V10/44 ,  G06V10/764 ,  G06V10/80

CPC classification number: G06V10/82 ,  G06F18/217 ,  G06F18/24133 ,  G06F18/254 ,  G06T7/0012 ,  G06T11/003 ,  G06V10/454 ,  G06V10/764 ,  G06V10/809 ,  G06T2207/10101 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30041 ,  G06V2201/03

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.