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公开(公告)号：US20200320399A1
公开(公告)日：2020-10-08
申请号：US16906034
申请日：2020-06-19
Applicant: Google LLC
Inventor： Yanping Huang , Alok Aggarwal , Quoc V. Le , Esteban Alberto Real
Abstract: A method for receiving training data for training a neural network (NN) to perform a machine learning (ML) task and for determining, using the training data, an optimized NN architecture for performing the ML task is described. Determining the optimized NN architecture includes: maintaining population data comprising, for each candidate architecture in a population of candidate architectures, (i) data defining the candidate architecture, and (ii) data specifying how recently a neural network having the candidate architecture has been trained while determining the optimized neural network architecture; and repeatedly performing multiple operations using each of a plurality of worker computing units to generate a new candidate architecture based on a selected candidate architecture having the best measure of fitness, adding the new candidate architecture to the population, and removing from the population the candidate architecture that was trained least recently.
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公开(公告)号：US20230259784A1
公开(公告)日：2023-08-17
申请号：US18140442
申请日：2023-04-27
Applicant: Google LLC
Inventor： Yanping Huang , Alok Aggarwal , Quoc V. Le , Esteban Alberto Real
Abstract: A method for receiving training data for training a neural network (NN) to perform a machine learning (ML) task and for determining, using the training data, an optimized NN architecture for performing the ML task is described. Determining the optimized NN architecture includes: maintaining population data comprising, for each candidate architecture in a population of candidate architectures, (i) data defining the candidate architecture, and (ii) data specifying how recently a neural network having the candidate architecture has been trained while determining the optimized neural network architecture; and repeatedly performing multiple operations using each of a plurality of worker computing units to generate a new candidate architecture based on a selected candidate architecture having the best measure of fitness, adding the new candidate architecture to the population, and removing from the population the candidate architecture that was trained least recently.
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公开(公告)号：US11669744B2
公开(公告)日：2023-06-06
申请号：US17475137
申请日：2021-09-14
Applicant: Google LLC
Inventor： Yanping Huang , Alok Aggarwal , Quoc V. Le , Esteban Alberto Real
Abstract: A method for receiving training data for training a neural network (NN) to perform a machine learning (ML) task and for determining, using the training data, an optimized NN architecture for performing the ML task is described. Determining the optimized NN architecture includes: maintaining population data comprising, for each candidate architecture in a population of candidate architectures, (i) data defining the candidate architecture, and (ii) data specifying how recently a neural network having the candidate architecture has been trained while determining the optimized neural network architecture; and repeatedly performing multiple operations using each of a plurality of worker computing units to generate a new candidate architecture based on a selected candidate architecture having the best measure of fitness, adding the new candidate architecture to the population, and removing from the population the candidate architecture that was trained least recently.
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Patent Agency Ranking
公开(公告)号：US20220004879A1
公开(公告)日：2022-01-06
申请号：US17475137
申请日：2021-09-14
Applicant: Google LLC
Inventor： Yanping Huang , Alok Aggarwal , Quoc V. Le , Esteban Alberto Real
Abstract: A method for receiving training data for training a neural network (NN) to perform a machine learning (ML) task and for determining, using the training data, an optimized NN architecture for performing the ML task is described. Determining the optimized NN architecture includes: maintaining population data comprising, for each candidate architecture in a population of candidate architectures, (i) data defining the candidate architecture, and (ii) data specifying how recently a neural network having the candidate architecture has been trained while determining the optimized neural network architecture; and repeatedly performing multiple operations using each of a plurality of worker computing units to generate a new candidate architecture based on a selected candidate architecture having the best measure of fitness, adding the new candidate architecture to the population, and removing from the population the candidate architecture that was trained least recently.
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公开(公告)号：US20250013881A1
公开(公告)日：2025-01-09
申请号：US18766415
申请日：2024-07-08
Applicant: Google LLC
Inventor： Yingjie Miao , John Dalton Co-Reyes , Esteban Alberto Real , George Jay Tucker , Aleksandra Faust
Abstract: Methods and systems for receiving training data for a machine learning (ML) task and searching, using the training data, for an optimized component of an ML algorithm for performing the ML task are described.
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公开(公告)号：US20230359895A1
公开(公告)日：2023-11-09
申请号：US18313291
申请日：2023-05-05
Applicant: Google LLC
Inventor： Xiangning Chen , Chen Liang , Da Huang , Esteban Alberto Real , Yao Liu , Kaiyuan Wang , Yifeng Lu , Quoc V. Le
IPC: G06N3/08
CPC classification number: G06N3/08
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to perform a machine learning task using a momentum and sign based optimizer.
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公开(公告)号：US20220383195A1
公开(公告)日：2022-12-01
申请号：US17795087
申请日：2021-02-08
Applicant: Google LLC
Inventor： Chen Liang , David Richard So , Esteban Alberto Real , Quoc V. Le
Abstract: A method for searching for an output machine learning (ML) algorithm to perform an ML task is described. The method includes: receiving a set of training examples and a set of validation examples, and generating a sequence of candidate ML algorithms to perform the task. For each candidate ML algorithm in the sequence, the method includes: setting up one or more training parameters for the candidate ML algorithm by executing a respective candidate setup function, training the candidate ML algorithm by processing the set of training examples using a respective candidate predict function and a respective candidate learn function, and evaluating a performance of the trained candidate ML algorithm by executing the respective candidate predict function on the set of validation examples to determine a performance metric. The method includes selecting a trained candidate ML algorithm with the best performance metric as the output ML algorithm for the task.
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公开(公告)号：US11144831B2
公开(公告)日：2021-10-12
申请号：US16906034
申请日：2020-06-19
Applicant: Google LLC
Inventor： Yanping Huang , Alok Aggarwal , Quoc V. Le , Esteban Alberto Real
Abstract: A method for receiving training data for training a neural network (NN) to perform a machine learning (ML) task and for determining, using the training data, an optimized NN architecture for performing the ML task is described. Determining the optimized NN architecture includes: maintaining population data comprising, for each candidate architecture in a population of candidate architectures, (i) data defining the candidate architecture, and (ii) data specifying how recently a neural network having the candidate architecture has been trained while determining the optimized neural network architecture; and repeatedly performing multiple operations using each of a plurality of worker computing units to generate a new candidate architecture based on a selected candidate architecture having the best measure of fitness, adding the new candidate architecture to the population, and removing from the population the candidate architecture that was trained least recently.
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