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    COMPUTATIONALLY EFFICIENT NEURAL NETWORK ARCHITECTURE SEARCH
    2.
    发明申请
    COMPUTATIONALLY EFFICIENT NEURAL NETWORK ARCHITECTURE SEARCH 有权

    公开(公告)号：US20210256390A1

    公开(公告)日：2021-08-19

    申请号：US17306813

    申请日：2021-05-03

    Applicant: Google LLC

    Inventor： David Martin Dohan ,  David Richard So ,  Chen Liang ,  Quoc V. Le

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A method for receiving training data for training a neural network to perform a machine learning task and for searching for, using the training data, an optimized neural network architecture for performing the machine learning task is described. Searching for the optimized neural network architecture includes: maintaining population data; maintaining threshold data; and repeatedly performing the following operations: selecting one or more candidate architectures from the population data; generating a new architecture from the one or more selected candidate architectures; for the new architecture: training a neural network having the new architecture until termination criteria for the training are satisfied; and determining a final measure of fitness of the neural network having the new architecture after the training; and adding data defining the new architecture and the final measure of fitness for the neural network having the new architecture to the population data.

    CODE-LEVEL NEURAL ARCHITECTURE SEARCH USING LANGUAGE MODELS
    5.
    发明公开
    CODE-LEVEL NEURAL ARCHITECTURE SEARCH USING LANGUAGE MODELS 审中-公开

    公开(公告)号：US20240273371A1

    公开(公告)日：2024-08-15

    申请号：US18431804

    申请日：2024-02-02

    Applicant: Google LLC

    Inventor： Angelica Chen ,  David Richard So ,  David Martin Dohan

    IPC: G06N3/086

    CPC classification number: G06N3/086

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining an architecture for a neural network configured to perform a machine learning task. In one aspect, a method comprises: receiving training data; searching for a final architecture of the neural network, wherein the searching comprises: maintaining current population data; and repeatedly performing evolutionary architecture search steps comprising: selecting one or more candidate architectures from the current population of candidate architectures defined by the source code included in the current population data; generating an input prompt; processing the input prompt using the language model neural network to generate output source code that defines a plurality of new candidate architectures; and using the plurality of new candidate architectures defined by the output source code to update the current population data.

    MACHINE LEARNING ALGORITHM SEARCH
    6.
    发明申请
    MACHINE LEARNING ALGORITHM SEARCH 有权

    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

    IPC: G06N20/00 ,  G06K9/62

    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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