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    Classifying data objects
    1.
    发明授权
    Classifying data objects 有权

    公开(公告)号：US11960519B2

    公开(公告)日：2024-04-16

    申请号：US16998891

    申请日：2020-08-20

    Applicant: Google LLC

    Inventor： Gregory Sean Corrado ,  Tomas Mikolov ,  Samy Bengio ,  Yoram Singer ,  Jonathon Shlens ,  Andrea L Frome ,  Jeffrey Adgate Dean ,  Mohammad Norouzi

    IPC: G06F16/35 ,  G06F16/50

    CPC classification number: G06F16/35 ,  G06F16/50

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying data objects. One of the methods includes obtaining data that associates each term in a vocabulary of terms with a respective high-dimensional representation of the term; obtaining classification data for a data object, wherein the classification data includes a respective score for each of a plurality of categories, and wherein each of the categories is associated with a respective category label; computing an aggregate high-dimensional representation for the data object from high-dimensional representations for the category labels associated with the categories and the respective scores; identifying a first term in the vocabulary of terms having a high-dimensional representation that is closest to the aggregate high-dimensional representation; and selecting the first term as a category label for the data object.

    Neural architecture search using a performance prediction neural network
    4.
    发明授权
    Neural architecture search using a performance prediction neural network 有权

    公开(公告)号：US11087201B2

    公开(公告)日：2021-08-10

    申请号：US16861491

    申请日：2020-04-29

    Applicant: Google LLC

    Inventor： Wei Hua ,  Barret Zoph ,  Jonathon Shlens ,  Chenxi Liu ,  Jonathan Huang ,  Jia Li ,  Fei-Fei Li ,  Kevin Patrick Murphy

    IPC: G06N3/04 ,  G06K9/62 ,  G06N3/063 ,  G06N3/08

    Abstract: A method for determining an architecture for a task neural network configured to perform a particular machine learning task is described. The method includes obtaining data specifying a current set of candidate architectures for the task neural network; for each candidate architecture in the current set: processing the data specifying the candidate architecture using a performance prediction neural network having multiple performance prediction parameters, the performance prediction neural network being configured to process the data specifying the candidate architecture in accordance with current values of the performance prediction parameters to generate a performance prediction that characterizes how well a neural network having the candidate architecture would perform after being trained on the particular machine learning task; and generating an updated set of candidate architectures by selecting one or more of the candidate architectures in the current set based on the performance predictions for the candidate architectures in the current set.

    STYLIZING INPUT IMAGES
    5.
    发明申请
    STYLIZING INPUT IMAGES 审中-公开

    公开(公告)号：US20200082578A1

    公开(公告)日：2020-03-12

    申请号：US16681391

    申请日：2019-11-12

    Applicant: Google LLC

    Inventor： Jonathon Shlens ,  Vincent Dumoulin ,  Manjunath Kudlur Venkatakrishna

    IPC: G06T11/00 ,  G06N3/04 ,  G06K9/62 ,  G06N3/08

    Abstract: A method for applying a style to an input image to generate a stylized image. The method includes maintaining data specifying respective parameter values for each image style in a set of image styles, receiving an input including an input image and data identifying an input style to be applied to the input image to generate a stylized image that is in the input style, determining, from the maintained data, parameter values for the input style, and generating the stylized image by processing the input image using a style transfer neural network that is configured to process the input image to generate the stylized image.

    Training neural networks using data augmentation policies
    6.
    发明授权
    Training neural networks using data augmentation policies 有权

    公开(公告)号：US11847541B2

    公开(公告)日：2023-12-19

    申请号：US17556871

    申请日：2021-12-20

    Applicant: Google LLC

    Inventor： Jonathon Shlens ,  Quoc V. Le ,  Ekin Dogus Cubuk ,  Barret Zoph

    IPC: G06N3/08 ,  G06F18/21 ,  G06F18/214 ,  G06N20/00 ,  G06N3/04

    CPC classification number: G06N20/00 ,  G06F18/214 ,  G06F18/217 ,  G06N3/08 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes obtaining a training data set for training a machine learning model, the training data set comprising a plurality of training inputs; determining a plurality of data augmentation policies, wherein each data augmentation policy defines a procedure for processing a training input to generate a transformed training input; for each data augmentation policy, training the machine learning model using the data augmentation policy; determining, for each data augmentation policy, a quality measure of the machine learning model that has been trained using the data augmentation policy; and selecting a final data augmentation policy based using the quality measures of the machine learning models.

    NEURAL ARCHITECTURE SEARCH FOR CONVOLUTIONAL NEURAL NETWORKS
    8.
    发明公开
    NEURAL ARCHITECTURE SEARCH FOR CONVOLUTIONAL NEURAL NETWORKS 审中-公开

    公开(公告)号：US20230252327A1

    公开(公告)日：2023-08-10

    申请号：US18137398

    申请日：2023-04-20

    Applicant: Google LLC

    Inventor： Vijay Vasudevan ,  Barret Zoph ,  Jonathon Shlens ,  Quoc V. Le

    IPC: G06N5/046 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06T7/00

    CPC classification number: G06N5/046 ,  G06N3/08 ,  G06N3/044 ,  G06N3/045 ,  G06T7/0002 ,  G06T2207/20084

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes generating, using a controller neural network having controller parameters and in accordance with current values of the controller parameters, a batch of output sequences. The method includes, for each output sequence in the batch: generating an instance of a child convolutional neural network (CNN) that includes multiple instances of a first convolutional cell having an architecture defined by the output sequence; training the instance of the child CNN to perform an image processing task; and evaluating a performance of the trained instance of the child CNN on the task to determine a performance metric for the trained instance of the child CNN; and using the performance metrics for the trained instances of the child CNN to adjust current values of the controller parameters of the controller neural network.

    Image segmentation using neural networks
    10.
    发明授权
    Image segmentation using neural networks 有权

    公开(公告)号：US11257217B2

    公开(公告)日：2022-02-22

    申请号：US16761381

    申请日：2018-11-20

    Applicant: Google LLC

    Inventor： Jonathon Shlens ,  Niruban Maheswaranathan ,  David Sussillo

    IPC: G06T7/11 ,  G06N3/04 ,  G06N3/08

    Abstract: A method for generating a segmentation of an image that assigns each pixel to a respective segmentation category from a set of segmentation categories is described. The method includes obtaining features of the image, the image including a plurality of pixels. For each of one or more time steps starting from an initial time step and continuing until a final time step, the method includes generating a network input from the features of the image and a current segmentation output as of the time step, processing the network input using a convolutional recurrent neural network to generate an intermediate segmentation output for the time step, and generating an updated segmentation output for the time step from the intermediate segmentation output for the time step and the current segmentation output as of the time step. The method includes generating a final segmentation of the image from the updated segmentation output.

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