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    MACHINE LEARNING ALGORITHM SEARCH
    2.
    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.

    Computationally efficient neural network architecture search
    3.
    发明授权
    Computationally efficient neural network architecture search 有权

    公开(公告)号：US10997503B2

    公开(公告)日：2021-05-04

    申请号：US16447866

    申请日：2019-06-20

    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.

    NEURAL QUESTION ANSWERING SYSTEM
    4.
    发明申请
    NEURAL QUESTION ANSWERING SYSTEM 审中-公开

    公开(公告)号：US20190130251A1

    公开(公告)日：2019-05-02

    申请号：US16176961

    申请日：2018-10-31

    Applicant: Google LLC

    Inventor： Ni Lao ,  Chen Liang ,  Quoc V. Le ,  John Blitzer

    IPC: G06N3/04 ,  G06F17/30 ,  G06F17/27 ,  G06K9/62

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a system output from a system input using a neural network system comprising an encoder neural network configured to, for each of a plurality of encoder time steps, receive an input sequence comprising a respective question token, and process the question token at the encoder time step to generate an encoded representation of the question token, and a decoder neural network configured to, for each of a plurality of decoder time steps, receive a decoder input, and process the decoder input and a preceding decoder hidden state to generate an updated decoder hidden state.

    COMPUTATIONALLY EFFICIENT NEURAL NETWORK ARCHITECTURE SEARCH
    6.
    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.

    LEARNING POLICIES USING SPARSE AND UNDERSPECIFIED REWARDS
    7.
    发明申请
    LEARNING POLICIES USING SPARSE AND UNDERSPECIFIED REWARDS 有权

    公开(公告)号：US20210256313A1

    公开(公告)日：2021-08-19

    申请号：US17180682

    申请日：2021-02-19

    Applicant: Google LLC

    Inventor： Rishabh Agarwal ,  Chen Liang ,  Dale Eric Schuurmans ,  Mohammad Norouzi

    IPC: G06K9/62 ,  G06N3/08

    Abstract: Methods and systems for learning policies using sparse and underspecified rewards. One of the methods includes training the policy jointly with an auxiliary reward function having a plurality of auxiliary reward parameters, the auxiliary reward function being configured to map, in accordance with the auxiliary reward parameters, trajectory features of at least a trajectory to an auxiliary reward value that indicates how well the trajectory performed a task in response to a context input.

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