公开(公告)号：US11625572B2

公开(公告)日：2023-04-11

申请号：US16610466

申请日：2018-05-03

Applicant: GOOGLE LLC

Inventor： Chung-Cheng Chiu ,  Navdeep Jaitly ,  John Dieterich Lawson ,  George Jay Tucker

IPC: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a target sequence from a source sequence. In one aspect, the system includes a recurrent neural network configured to, at each time step, receive an input for the time step and process the input to generate a progress score and a set of output scores; and a subsystem configured to, at each time step, generate the recurrent neural network input and provide the input to the recurrent neural network; determine, from the progress score, whether or not to emit a new output at the time step; and, in response to determining to emit a new output, select an output using the output scores and emit the selected output as the output at a next position in the output order.

公开(公告)号：US11790211B2

公开(公告)日：2023-10-17

申请号：US15884253

申请日：2018-01-30

Applicant: Google LLC

Inventor： Augustus Quadrozzi Odena ,  John Dieterich Lawson

IPC: G06N3/045 ,  G06N3/08 ,  G06N3/044

CPC classification number: G06N3/045 ,  G06N3/044 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for adjusting neural network resource usage. One of the methods includes receiving a network input for processing by a task neural network, the task neural network comprising a plurality of neural network layers; receiving a usage input specifying a respective weight for each of one or more usage factors, wherein each usage factor impacts how many computational resources are used by the task neural network during the processing of the network input; and processing the network input using the task neural network in accordance with the usage input to generate a network output for the network input, comprising: selecting, based at least on the usage input, a proper subset of the plurality of neural network layers to be active while processing the network input, and processing the network input using only the selected neural network layers.

公开(公告)号：US20190236438A1

公开(公告)日：2019-08-01

申请号：US15884253

申请日：2018-01-30

Applicant: Google LLC

Inventor： Augustus Quadrozzi Odena ,  John Dieterich Lawson

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/0454 ,  G06N3/0445 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for adjusting neural network resource usage. One of the methods includes receiving a network input for processing by a task neural network, the task neural network comprising a plurality of neural network layers; receiving a usage input specifying a respective weight for each of one or more usage factors, wherein each usage factor impacts how many computational resources are used by the task neural network during the processing of the network input; and processing the network input using the task neural network in accordance with the usage input to generate a network output for the network input, comprising: selecting, based at least on the usage input, a proper subset of the plurality of neural network layers to be active while processing the network input, and processing the network input using only the selected neural network layers.

公开(公告)号：US20240185030A1

公开(公告)日：2024-06-06

申请号：US18487802

申请日：2023-10-16

Applicant: Google LLC

Inventor： Augustus Quadrozzi Odena ,  John Dieterich Lawson

IPC: G06N3/045 ,  G06N3/044 ,  G06N3/08

CPC classification number: G06N3/045 ,  G06N3/044 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for adjusting neural network resource usage. One of the methods includes receiving a network input for processing by a task neural network, the task neural network comprising a plurality of neural network layers; receiving a usage input specifying a respective weight for each of one or more usage factors, wherein each usage factor impacts how many computational resources are used by the task neural network during the processing of the network input; and processing the network input using the task neural network in accordance with the usage input to generate a network output for the network input, comprising: selecting, based at least on the usage input, a proper subset of the plurality of neural network layers to be active while processing the network input, and processing the network input using only the selected neural network layers.

公开(公告)号：US20200151544A1

公开(公告)日：2020-05-14

申请号：US16610466

申请日：2018-05-03

Applicant: GOOGLE LLC

Inventor： Chung-Cheng Chiu ,  Navdeep Jaitly ,  John Dieterich Lawson ,  George Jay Tucker

IPC: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a target sequence from a source sequence. In one aspect, the system includes a recurrent neural network configured to, at each time step, receive an input for the time step and process the input to generate a progress score and a set of output scores; and a subsystem configured to, at each time step, generate the recurrent neural network input and provide the input to the recurrent neural network; determine, from the progress score, whether or not to emit a new output at the time step; and, in response to determining to emit a new output, select an output using the output scores and emit the selected output as the output at a next position in the output order.