公开(公告)号：US11481638B2

公开(公告)日：2022-10-25

申请号：US16645441

申请日：2018-09-12

Applicant: GOOGLE LLC

Inventor： Sherry Moore ,  Jeremiah Harmsen ,  Noah Fiedel

IPC: G06N3/10 ,  G06F8/34 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting a neural network with additional operations. One of the methods includes maintaining, by a computational graph system that manages execution of computational graphs representing neural network operations for users of the computational graph system, data specifying a plurality of pre-trained neural networks, wherein each of the pre-trained neural networks is a neural network that has been trained on training data to determine trained values of the respective parameters of the neural network; obtaining data specifying a user computational graph representing neural network operations, the user computational graph comprising a plurality of nodes connected by edges; identifying (i) an insertion point after a first node in the user computational graph and (ii) a particular pre-trained neural network from the plurality of pre-trained neural networks; and inserting a remote call node into the user computational graph.

公开(公告)号：US20230119229A1

公开(公告)日：2023-04-20

申请号：US17972492

申请日：2022-10-24

Applicant: Google LLC

Inventor： Sherry Moore ,  Jeremiah Harmsen ,  Noah Fiedel

IPC: G06N3/10 ,  G06F8/34 ,  G06N3/08 ,  G06F18/214 ,  G06F18/2137 ,  G06F18/21 ,  G06N3/044 ,  G06N3/047

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting a neural network with additional operations. One of the methods includes maintaining, by a computational graph system that manages execution of computational graphs representing neural network operations for users of the computational graph system, data specifying a plurality of pre-trained neural networks, wherein each of the pre-trained neural networks is a neural network that has been trained on training data to determine trained values of the respective parameters of the neural network; obtaining data specifying a user computational graph representing neural network operations, the user computational graph comprising a plurality of nodes connected by edges; identifying (i) an insertion point after a first node in the user computational graph and (ii) a particular pre-trained neural network from the plurality of pre-trained neural networks; and inserting a remote call node into the user computational graph.

公开(公告)号：US11900263B2

公开(公告)日：2024-02-13

申请号：US17972492

申请日：2022-10-24

Applicant: Google LLC

Inventor： Sherry Moore ,  Jeremiah Harmsen ,  Noah Fiedel

IPC: G06N3/10 ,  G06F8/34 ,  G06N3/08 ,  G06F18/214 ,  G06F18/2137 ,  G06F18/21 ,  G06N3/044 ,  G06N3/047

CPC classification number: G06N3/105 ,  G06F8/34 ,  G06F18/214 ,  G06F18/2137 ,  G06F18/2178 ,  G06N3/044 ,  G06N3/047 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting a neural network with additional operations. One of the methods includes maintaining, by a computational graph system that manages execution of computational graphs representing neural network operations for users of the computational graph system, data specifying a plurality of pre-trained neural networks, wherein each of the pre-trained neural networks is a neural network that has been trained on training data to determine trained values of the respective parameters of the neural network; obtaining data specifying a user computational graph representing neural network operations, the user computational graph comprising a plurality of nodes connected by edges; identifying (i) an insertion point after a first node in the user computational graph and (ii) a particular pre-trained neural network from the plurality of pre-trained neural networks; and inserting a remote call node into the user computational graph.

公开(公告)号：US10789544B2

公开(公告)日：2020-09-29

申请号：US15091381

申请日：2016-04-05

Applicant: Google LLC

Inventor： Noah Fiedel ,  Christopher Olston ,  Jeremiah Harmsen

IPC: G06N20/00 ,  G06F9/54

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for batching inputs to machine learning models. One of the methods includes receiving a stream of requests, each request identifying a respective input for processing by a first machine learning model; adding the respective input from each request to a first queue of inputs for processing by the first machine learning model; determining, at a first time, that a count of inputs in the first queue as of the first time equals or exceeds a maximum batch size and, in response: generating a first batched input from the inputs in the queue as of the first time so that a count of inputs in the first batched input equals the maximum batch size, and providing the first batched input for processing by the first machine learning model.

公开(公告)号：US20200210851A1

公开(公告)日：2020-07-02

申请号：US16645441

申请日：2018-09-12

Applicant: GOOGLE LLC

Inventor： Sherry Moore ,  Jeremiah Harmsen ,  Noah Fiedel

IPC: G06N3/10 ,  G06N3/04 ,  G06N3/08 ,  G06K9/62 ,  G06F8/34

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting a neural network with additional operations. One of the methods includes maintaining, by a computational graph system that manages execution of computational graphs representing neural network operations for users of the computational graph system, data specifying a plurality of pre-trained neural networks, wherein each of the pre-trained neural networks is a neural network that has been trained on training data to determine trained values of the respective parameters of the neural network; obtaining data specifying a user computational graph representing neural network operations, the user computational graph comprising a plurality of nodes connected by edges; identifying (i) an insertion point after a first node in the user computational graph and (ii) a particular pre-trained neural network from the plurality of pre-trained neural networks; and inserting a remote call node into the user computational graph.