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    Predicting Machine-Learned Model Performance from the Parameter Values of the Model
    3.
    发明申请
    Predicting Machine-Learned Model Performance from the Parameter Values of the Model 有权

    公开(公告)号：US20210256422A1

    公开(公告)日：2021-08-19

    申请号：US17177362

    申请日：2021-02-17

    Applicant: Google LLC

    Inventor： Thomas Unterthiner ,  Daniel Martin Keysers ,  Sylvain Gelly ,  Olivier Jean Andre Bousquet ,  Ilya Tolstikhin

    IPC: G06N20/00 ,  G06K9/62

    Abstract: Provided are systems and methods for predicting machine learning model performance from the model parameter values, including for use in making improved decisions with regard to early stopping of training procedures. As one example, the present disclosure discusses the prediction of the accuracy (e.g., relative to a defined task and testing dataset such as a computer vision task) of trained neural networks (e.g., convolutional neural networks (CNNs)), using only the parameter values (e.g., the values of the network's weights) as inputs. As such, one example aspect of the present disclosure is directed to computing systems that include and use a machine-learned performance prediction model that has been trained to predict performance values of machine-learned models based on their parameter values (e.g., weight values and/or hyperparameter values).

    END-TO-END TRAINING OF NEURAL NETWORKS FOR IMAGE PROCESSING
    9.
    发明申请
    END-TO-END TRAINING OF NEURAL NETWORKS FOR IMAGE PROCESSING 有权

    公开(公告)号：US20220172066A1

    公开(公告)日：2022-06-02

    申请号：US17538891

    申请日：2021-11-30

    Applicant: Google LLC

    Inventor： Thomas Unterthiner ,  Alexey Dosovitskiy ,  Aravindh Mahendran ,  Dirk Weissenborn ,  Jakob D. Uszkoreit ,  Jean-Baptiste Cordonnier

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

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to process images. One of the methods includes obtaining a training image; processing the training image using a first subnetwork to generate, for each of a plurality of first image patches of the training image, a relevance score; generating, using the relevance scores, one or more second image patches of the training image by performing one or more differentiable operations on the relevance scores; processing the one or more second image patches using a second subnetwork to generate a prediction about the training image; determining an error of the training network output; and generating a parameter update for the first subnetwork, comprising backpropagating gradients determined according to the error of the training network output through i) the second subnetwork, ii) the one or more differentiable operations, and iii) the first subnetwork.

    Object-Centric Learning with Slot Attention
    10.
    发明申请
    Object-Centric Learning with Slot Attention 有权

    公开(公告)号：US20210383199A1

    公开(公告)日：2021-12-09

    申请号：US16927018

    申请日：2020-07-13

    Applicant: Google LLC

    Inventor： Dirk Weissenborn ,  Jakob Uszkoreit ,  Thomas Unterthiner ,  Aravindh Mahendran ,  Francesco Locatello ,  Thomas Kipf ,  Georg Heigold ,  Alexey Dosovitskiy

    IPC: G06N3/063 ,  G06N3/08 ,  G06F17/16

    Abstract: A method involves receiving a perceptual representation including a plurality of feature vectors, and initializing a plurality of slot vectors represented by a neural network memory unit. Each respective slot vector is configured to represent a corresponding entity in the perceptual representation. The method also involves determining an attention matrix based on a product of the plurality of feature vectors transformed by a key function and the plurality of slot vectors transformed by a query function. Each respective value of a plurality of values along each respective dimension of the attention matrix is normalized with respect to the plurality of values. The method additionally involves determining an update matrix based on the plurality of feature vectors transformed by a value function and the attention matrix, and updating the plurality of slot vectors based on the update matrix by way of the neural network memory unit.

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