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公开(公告)号：US11074454B1
公开(公告)日：2021-07-27
申请号：US16410863
申请日：2019-05-13
Applicant: Google LLC
Inventor： Sudheendra Vijayanarasimhan , George Dan Toderici , Yue Hei Ng , Matthew John Hausknecht , Oriol Vinyals , Rajat Monga
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying videos using neural networks. One of the methods includes obtaining a temporal sequence of video frames, wherein the temporal sequence comprises a respective video frame from a particular video at each of a plurality time steps; for each time step of the plurality of time steps: processing the video frame at the time step using a convolutional neural network to generate features of the video frame; and processing the features of the video frame using an LSTM neural network to generate a set of label scores for the time step and classifying the video as relating to one or more of the topics represented by labels in the set of labels from the label scores for each of the plurality of time steps.
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公开(公告)号：US10733535B1
公开(公告)日：2020-08-04
申请号：US15665236
申请日：2017-07-31
Applicant: Google LLC
Inventor： Gregory S. Corrado , Kai Chen , Jeffrey A. Dean , Samy Bengio , Rajat Monga , Matthieu Devin
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a model using parameter server shards. One of the methods includes receiving, at a parameter server shard configured to maintain values of a disjoint partition of the parameters of the model, a succession of respective requests for parameter values from each of a plurality of replicas of the model; in response to each request, downloading a current value of each requested parameter to the replica from which the request was received; receiving a succession of uploads, each upload including respective delta values for each of the parameters in the partition maintained by the shard; and updating values of the parameters in the partition maintained by the parameter server shard repeatedly based on the uploads of delta values to generate current parameter values.
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公开(公告)号：US11687832B1
公开(公告)日：2023-06-27
申请号：US16983979
申请日：2020-08-03
Applicant: Google LLC
Inventor： Gregory S. Corrado , Kai Chen , Jeffrey A. Dean , Samy Bengio , Rajat Monga , Matthieu Devin
IPC: G06N20/00 , G06N3/063 , G06N3/08 , G06N7/08 , G06N5/025 , G06F18/214 , G06F18/2411 , G06N7/01
CPC classification number: G06N20/00 , G06N3/063 , G06N3/08 , G06N7/08 , G06F18/214 , G06F18/2411 , G06N5/025 , G06N7/01
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a model using parameter server shards. One of the methods includes receiving, at a parameter server shard configured to maintain values of a disjoint partition of the parameters of the model, a succession of respective requests for parameter values from each of a plurality of replicas of the model; in response to each request, downloading a current value of each requested parameter to the replica from which the request was received; receiving a succession of uploads, each upload including respective delta values for each of the parameters in the partition maintained by the shard; and updating values of the parameters in the partition maintained by the parameter server shard repeatedly based on the uploads of delta values to generate current parameter values.
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公开(公告)号：US10289912B1
公开(公告)日：2019-05-14
申请号：US15143218
申请日：2016-04-29
Applicant: Google LLC
Inventor： Sudheendra Vijayanarasimhan , George Dan Toderici , Yue Hei Ng , Matthew John Hausknecht , Oriol Vinyals , Rajat Monga
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying videos using neural networks. One of the methods includes obtaining a temporal sequence of video frames, wherein the temporal sequence comprises a respective video frame from a particular video at each of a plurality time steps; for each time step of the plurality of time steps: processing the video frame at the time step using a convolutional neural network to generate features of the video frame; and processing the features of the video frame using an LSTM neural network to generate a set of label scores for the time step and classifying the video as relating to one or more of the topics represented by labels in the set of labels from the label scores for each of the plurality of time steps.
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